~?l Lander, E. S. Linton, L. M. Birren, B. Nusbaum, C. Zody, M. C. Baldwin, J. Devon, K. Dewar, K. Doyle, M. FitzHugh, W. Funke, R. Gage, D. Harris, K. Heaford, A. Howland, J. Kann, L. Lehoczky, J. LeVine, R. McEwan, P. McKernan, K. Meldrim, J. Mesirov, J. P. Miranda, C. Morris, W. Naylor, J. Raymond, C. Rosetti, M. Santos, R. Sheridan, A. Sougnez, C. Stange-Thomann, N. Stojanovic, N. Subramanian, A. Wyman, D. Rogers, J. Sulston, J. Ainscough, R. Beck, S. Bentley, D. Burton, J. Clee, C. Carter, N. Coulson, A. Deadman, R. Deloukas, P. Dunham, A. Dunham, I. Durbin, R. French, L. Grafham, D. Gregory, S. Hubbard, T. Humphray, S. Hunt, A. Jones, M. Lloyd, C. McMurray, A. Matthews, L. Mercer, S. Milne, S. Mullikin, J. C. Mungall, A. Plumb, R. Ross, M. Shownkeen, R. Sims, S. Waterston, R. H. Wilson, R. K. Hillier, L. W. McPherson, J. D. Marra, M. A. Mardis, E. R. Fulton, L. A. Chinwalla, A. T. Pepin, K. H. Gish, W. R. Chissoe, S. L. Wendl, M. C. Delehaunty, K. D. Miner, T. L. Delehaunty, A. Kramer, J. B. Cook, L. L. Fulton, R. S. Johnson, D. L. Minx, P. J. Clifton, S. W. Hawkins, T. Branscomb, E. Predki, P. Richardson, P. Wenning, S. Slezak, T. Doggett, N. Cheng, J. F. Olsen, A. Lucas, S. Elkin, C. Uberbacher, E. Frazier, M. Gibbs, R. A. Muzny, D. M. Scherer, S. E. Bouck, J. B. Sodergren, E. J. Worley, K. C. Rives, C. M. Gorrell, J. H. Metzker, M. L. Naylor, S. L. Kucherlapati, R. S. Nelson, D. L. Weinstock, G. M. Sakaki, Y. Fujiyama, A. Hattori, M. Yada, T. Toyoda, A. Itoh, T. Kawagoe, C. Watanabe, H. Totoki, Y. Taylor, T. Weissenbach, J. Heilig, R. Saurin, W. Artiguenave, F. Brottier, P. Bruls, T. Pelletier, E. Robert, C. Wincker, P. Smith, D. R. Doucette-Stamm, L. Rubenfield, M. Weinstock, K. Lee, H. M. Dubois, J. Rosenthal, A. Platzer, M. Nyakatura, G. Taudien, S. Rump, A. Yang, H. Yu, J. Wang, J. Huang, G. Gu, J. Hood, L. Rowen, L. Madan, A. Qin, S. Davis, R. W. Federspiel, N. A. Abola, A. P. Proctor, M. J. Myers, R. M. Schmutz, J. Dickson, M. Grimwood, J. Cox, D. R. Olson, M. V. Kaul, R. Raymond, C. Shimizu, N. Kawasaki, K. Minoshima, S. Evans, G. A. Athanasiou, M. Schultz, R. Roe, B. A. Chen, F. Pan, H. Ramser, J. Lehrach, H. Reinhardt, R. McCombie, W. R. de la Bastide, M. Dedhia, N. Blocker, H. Hornischer, K. Nordsiek, G. Agarwala, R. Aravind, L. Bailey, J. A. Bateman, A. Batzoglou, S. Birney, E. Bork, P. Brown, D. G. Burge, C. B. Cerutti, L. Chen, H. C. Church, D. Clamp, M. Copley, R. R. Doerks, T. Eddy, S. R. Eichler, E. E. Furey, T. S. Galagan, J. Gilbert, J. G. Harmon, C. Hayashizaki, Y. Haussler, D. Hermjakob, H. Hokamp, K. Jang, W. Johnson, L. S. Jones, T. A. Kasif, S. Kaspryzk, A. Kennedy, S. Kent, W. J. Kitts, P. Koonin, E. V. Korf, I. Kulp, D. Lancet, D. Lowe, T. M. McLysaght, A. Mikkelsen, T. Moran, J. V. Mulder, N. Pollara, V. J. Ponting, C. P. Schuler, G. Schultz, J. Slater, G. Smit, A. F. Stupka, E. Szustakowski, J. Thierry-Mieg, D. Thierry-Mieg, J. Wagner, L. Wallis, J. Wheeler, R. Williams, A. Wolf, Y. I. Wolfe, K. H. Yang, S. P. Yeh, R. F. Collins, F. Guyer, M. S. Peterson, J. Felsenfeld, A. Wetterstrand, K. A. Patrinos, A. Morgan, M. J. de Jong, P. Catanese, J. J. Osoegawa, K. Shizuya, H. Choi, S. Chen, Y. J.20013Initial sequencing and analysis of the human genome860-921Nature4096822Animals Chromosome Mapping Conserved Sequence CpG Islands DNA Transposable Elements Databases, Factual Drug Industry Evolution, Molecular Forecasting GC Rich Sequence Gene Duplication Genes Genetic Diseases, Inborn Genetics, Medical *Genome, Human *Human Genome Project Humans Mutation Private Sector Proteins/genetics Proteome Public Sector RNA/genetics Repetitive Sequences, Nucleic Acid *Sequence Analysis, DNA/methods Species SpecificityFeb 15yThe human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11237011 0028-0836 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.11237011Whitehead Institute for Biomedical Research, Center for Genome Research, Cambridge, Massachusetts 02142, USA. lander@genome.wi.mit.edu~?" Venter, J. C. Adams, M. D. Myers, E. W. Li, P. W. Mural, R. J. Sutton, G. G. Smith, H. O. Yandell, M. Evans, C. A. Holt, R. A. Gocayne, J. D. Amanatides, P. Ballew, R. M. Huson, D. H. Wortman, J. R. Zhang, Q. Kodira, C. D. Zheng, X. H. Chen, L. Skupski, M. Subramanian, G. Thomas, P. D. Zhang, J. Gabor Miklos, G. L. Nelson, C. Broder, S. Clark, A. G. Nadeau, J. McKusick, V. A. Zinder, N. Levine, A. J. Roberts, R. J. Simon, M. Slayman, C. Hunkapiller, M. Bolanos, R. Delcher, A. Dew, I. Fasulo, D. Flanigan, M. Florea, L. Halpern, A. Hannenhalli, S. Kravitz, S. Levy, S. Mobarry, C. Reinert, K. Remington, K. Abu-Threideh, J. Beasley, E. Biddick, K. Bonazzi, V. Brandon, R. Cargill, M. Chandramouliswaran, I. Charlab, R. Chaturvedi, K. Deng, Z. Di Francesco, V. Dunn, P. Eilbeck, K. Evangelista, C. Gabrielian, A. E. Gan, W. Ge, W. Gong, F. Gu, Z. Guan, P. Heiman, T. J. Higgins, M. E. Ji, R. R. Ke, Z. Ketchum, K. A. Lai, Z. Lei, Y. Li, Z. Li, J. Liang, Y. Lin, X. Lu, F. Merkulov, G. V. Milshina, N. Moore, H. M. Naik, A. K. Narayan, V. A. Neelam, B. Nusskern, D. Rusch, D. B. Salzberg, S. Shao, W. Shue, B. Sun, J. Wang, Z. Wang, A. Wang, X. Wang, J. Wei, M. Wides, R. Xiao, C. Yan, C. Yao, A. Ye, J. Zhan, M. Zhang, W. Zhang, H. Zhao, Q. Zheng, L. Zhong, F. Zhong, W. Zhu, S. Zhao, S. Gilbert, D. Baumhueter, S. Spier, G. Carter, C. Cravchik, A. Woodage, T. Ali, F. An, H. Awe, A. Baldwin, D. Baden, H. Barnstead, M. Barrow, I. Beeson, K. Busam, D. Carver, A. Center, A. Cheng, M. L. Curry, L. Danaher, S. Davenport, L. Desilets, R. Dietz, S. Dodson, K. Doup, L. Ferriera, S. Garg, N. Gluecksmann, A. Hart, B. Haynes, J. Haynes, C. Heiner, C. Hladun, S. Hostin, D. Houck, J. Howland, T. Ibegwam, C. Johnson, J. Kalush, F. Kline, L. Koduru, S. Love, A. Mann, F. May, D. McCawley, S. McIntosh, T. McMullen, I. Moy, M. Moy, L. Murphy, B. Nelson, K. Pfannkoch, C. Pratts, E. Puri, V. Qureshi, H. Reardon, M. Rodriguez, R. Rogers, Y. H. Romblad, D. Ruhfel, B. Scott, R. Sitter, C. Smallwood, M. Stewart, E. Strong, R. Suh, E. Thomas, R. Tint, N. N. Tse, S. Vech, C. Wang, G. Wetter, J. Williams, S. Williams, M. Windsor, S. Winn-Deen, E. Wolfe, K. Zaveri, J. Zaveri, K. Abril, J. F. Guigo, R. Campbell, M. J. Sjolander, K. V. Karlak, B. Kejariwal, A. Mi, H. Lazareva, B. Hatton, T. Narechania, A. Diemer, K. Muruganujan, A. Guo, N. Sato, S. Bafna, V. Istrail, S. Lippert, R. Schwartz, R. Walenz, B. Yooseph, S. Allen, D. Basu, A. Baxendale, J. Blick, L. Caminha, M. Carnes-Stine, J. Caulk, P. Chiang, Y. H. Coyne, M. Dahlke, C. Mays, A. Dombroski, M. Donnelly, M. Ely, D. Esparham, S. Fosler, C. Gire, H. Glanowski, S. Glasser, K. Glodek, A. Gorokhov, M. Graham, K. Gropman, B. Harris, M. Heil, J. Henderson, S. Hoover, J. Jennings, D. Jordan, C. Jordan, J. Kasha, J. Kagan, L. Kraft, C. Levitsky, A. Lewis, M. Liu, X. Lopez, J. Ma, D. Majoros, W. McDaniel, J. Murphy, S. Newman, M. Nguyen, T. Nguyen, N. Nodell, M. Pan, S. Peck, J. Peterson, M. Rowe, W. Sanders, R. Scott, J. Simpson, M. Smith, T. Sprague, A. Stockwell, T. Turner, R. Venter, E. Wang, M. Wen, M. Wu, D. Wu, M. Xia, A. Zandieh, A. Zhu, X.2001 The sequence of the human genome1304-51Science2915507Algorithms Animals Chromosome Banding Chromosome Mapping Chromosomes, Artificial, Bacterial Computational Biology Consensus Sequence CpG Islands DNA, Intergenic Databases, Factual Evolution, Molecular Exons Female Gene Duplication Genes *Genome, Human *Human Genome Project Humans Introns Male Phenotype Physical Chromosome Mapping Polymorphism, Single Nucleotide Proteins/genetics/physiology Pseudogenes Repetitive Sequences, Nucleic Acid Retroelements *Sequence Analysis, DNA/methods Species Specificity Variation (Genetics)Feb 16_ A 2.91-billion base pair (bp) consensus sequence of the euchromatic portion of the human genome was generated by the whole-genome shotgun sequencing method. The 14.8-billion bp DNA sequence was generated over 9 months from 27,271,853 high-quality sequence reads (5.11-fold coverage of the genome) from both ends of plasmid clones made from the DNA of five individuals. Two assembly strategies-a whole-genome assembly and a regional chromosome assembly-were used, each combining sequence data from Celera and the publicly funded genome effort. The public data were shredded into 550-bp segments to create a 2.9-fold coverage of those genome regions that had been sequenced, without including biases inherent in the cloning and assembly procedure used by the publicly funded group. This brought the effective coverage in the assemblies to eightfold, reducing the number and size of gaps in the final assembly over what would be obtained with 5.11-fold coverage. The two assembly strategies yielded very similar results that largely agree with independent mapping data. The assemblies effectively cover the euchromatic regions of the human chromosomes. More than 90% of the genome is in scaffold assemblies of 100,000 bp or more, and 25% of the genome is in scaffolds of 10 million bp or larger. Analysis of the genome sequence revealed 26,588 protein-encoding transcripts for which there was strong corroborating evidence and an additional approximately 12,000 computationally derived genes with mouse matches or other weak supporting evidence. Although gene-dense clusters are obvious, almost half the genes are dispersed in low G+C sequence separated by large tracts of apparently noncoding sequence. Only 1.1% of the genome is spanned by exons, whereas 24% is in introns, with 75% of the genome being intergenic DNA. Duplications of segmental blocks, ranging in size up to chromosomal lengths, are abundant throughout the genome and reveal a complex evolutionary history. Comparative genomic analysis indicates vertebrate expansions of genes associated with neuronal function, with tissue-specific developmental regulation, and with the hemostasis and immune systems. DNA sequence comparisons between the consensus sequence and publicly funded genome data provided locations of 2.1 million single-nucleotide polymorphisms (SNPs). A random pair of human haploid genomes differed at a rate of 1 bp per 1250 on average, but there was marked heterogeneity in the level of polymorphism across the genome. Less than 1% of all SNPs resulted in variation in proteins, but the task of determining which SNPs have functional consequences remains an open challenge.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11181995 B0036-8075 (Print) Journal Article Research Support, Non-U.S. Gov't11181995UCelera Genomics, 45 West Gude Drive, Rockville, MD 20850, USA. humangenome@celera.com2~?'Hamer, D. Sirota, L.2000Beware the chopsticks gene11-3Mol Psychiatry51mEthnic Groups/*genetics *Genetics, Population Humans *Linkage (Genetics) Motor Skills Neurobiology/*standardsJanPopulation stratification is a potential source of error in psychiatric genetics. New study designs and statistical methods can help guard against this problem. Molecular Psychiatry (2000) 5, 11-13.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10673763 1359-4184 (Print) News10673763~?)Sasieni, P. D.19971From genotypes to genes: doubling the sample size1253-61 Biometrics5342Alleles Carcinoma, Squamous Cell/genetics/immunology *Case-Control Studies Chi-Square Distribution Female Genetic Diseases, Inborn/epidemiology/*genetics *Genotype HLA-DQ Antigens/genetics Heterozygote Homozygote Humans Models, Genetic Odds Ratio *Sample Size Uterine Cervical Neoplasms/genetics/immunologyDecThis paper considers the analysis of genetic case-control data. One approach considers the allele frequency in cases and controls. Because each individual has two alleles at any autosomal locus, there will be twice as many alleles as people. Another approach considers the risk of the disease in those who do not have the allele of interest (A), those who have a single copy (heterozygous), and those who are homozygous for A. A third approach does not differentiate between individuals with one or two copies of A. This was common when alleles were determined serologically and one could not distinguish between homozygotes and those with one copy of A and one of an unknown allele. All three approaches have been used in the literature, but this is the first systematic comparison of them. The different interpretations of the odds ratios from such analyses are explored and conditions are given under which the first two approaches are asymptotically equivalent. The chi-squared statistics from the three approaches are discussed. Both the odds ratio and the chi-squared statistic from the analysis that treats alleles rather than genotypes as individual entities are appropriate only when the Hardy-Weinberg equilibrium holds. When the equilibrium holds, the allele-based test statistic is asymptotically equivalent to the test for trend using the genotype data. Thus, analyses that treat alleles rather than people as observations should not be used. Instead, we recommend that such data should be analyzed by genotype.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9423247 !0006-341X (Print) Journal Article9423247cDepartment of Mathematics, Statistics and Epidemiology, Imperial Cancer Research Fund, London, U.K. ?!Curtis, D. Knight, J. Sham, P. C.2006-Program report: GENECOUNTING support programs277-9 Ann Hum Genet70Pt 2%*Databases, Genetic Haplotypes HumansMarWe describe a suite of programs which enhance the usability of GENECOUNTING, a program for estimating haplotype frequencies in unrelated subjects. The programs, called RUNGC, SCANASSOC, COMPGR, SCANGROUP and LDPAIRS, carry out likelihood ratio tests and permutation tests to detect differences in haplotype frequencies between cases and controls,or between predefined groups, and output likely haplotype assignments and tables of linkage disequilibrium statistics between all pairs of markers in a dataset.Journal ArticleVDepartment of Adult Psychiatry, Royal London Hospital, Whitechapel, London E1 1BB, UK.X?NDurrant, C. Zondervan, K. T. Cardon, L. R. Hunt, S. Deloukas, P. Morris, A. P.2004bLinkage disequilibrium mapping via cladistic analysis of single-nucleotide polymorphism haplotypes35-43Am J Hum Genet751*Chromosome Mapping Computer Simulation Genetics, Population Haplotypes/*genetics Human *Linkage Disequilibrium *Models, Genetic Polymorphism, Single Nucleotide/*genetics Support, Non-U.S. Gov'tJul/We present a novel approach to disease-gene mapping via cladistic analysis of single-nucleotide polymorphism (SNP) haplotypes obtained from large-scale, population-based association studies, applicable to whole-genome screens, candidate-gene studies, or fine-scale mapping. Clades of haplotypes are tested for association with disease, exploiting the expected similarity of chromosomes with recent shared ancestry in the region flanking the disease gene. The method is developed in a logistic-regression framework and can easily incorporate covariates such as environmental risk factors or additional unlinked loci to allow for population structure. To evaluate the power of this approach to detect disease-marker association, we have developed a simulation algorithm to generate high-density SNP data with short-range linkage disequilibrium based on empirical patterns of haplotype diversity. The results of the simulation study highlight substantial gains in power over single-locus tests for a wide range of disease models, despite overcorrection for multiple testing.HCJournal ArticleWWellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom. ?Fallin, D. Schork, N. J.2000Accuracy of haplotype frequency estimation for biallelic loci, via the expectation-maximization algorithm for unphased diploid genotype data947-59Am J Hum Genet674H*Algorithms *Alleles *Computer Simulation *Diploidy Gene Frequency/*genetics Genotype Haplotypes/*genetics Humans Likelihood Functions Linkage Disequilibrium/genetics Polymorphism, Single Nucleotide/genetics Regression Analysis Research Support, U.S. Gov't, P.H.S. Sample Size Selection Bias Sensitivity and Specificity SoftwareOctHaplotype analyses have become increasingly common in genetic studies of human disease because of their ability to identify unique chromosomal segments likely to harbor disease-predisposing genes. The study of haplotypes is also used to investigate many population processes, such as migration and immigration rates, linkage-disequilibrium strength, and the relatedness of populations. Unfortunately, many haplotype-analysis methods require phase information that can be difficult to obtain from samples of nonhaploid species. There are, however, strategies for estimating haplotype frequencies from unphased diploid genotype data collected on a sample of individuals that make use of the expectation-maximization (EM) algorithm to overcome the missing phase information. The accuracy of such strategies, compared with other phase-determination methods, must be assessed before their use can be advocated. In this study, we consider and explore sources of error between EM-derived haplotype frequency estimates and their population parameters, noting that much of this error is due to sampling error, which is inherent in all studies, even when phase can be determined. In light of this, we focus on the additional error between haplotype frequencies within a sample data set and EM-derived haplotype frequency estimates incurred by the estimation procedure. We assess the accuracy of haplotype frequency estimation as a function of a number of factors, including sample size, number of loci studied, allele frequencies, and locus-specific allelic departures from Hardy-Weinberg and linkage equilibrium. We point out the relative impacts of sampling error and estimation error, calling attention to the pronounced accuracy of EM estimates once sampling error has been accounted for. We also suggest that many factors that may influence accuracy can be assessed empirically within a data set-a fact that can be used to create "diagnostics" that a user can turn to for assessing potential inaccuracies in estimation.Journal Article}Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44109, USA. dfallin@hal.cwru.edu ?'Long, J. C. Williams, R. C. Urbanek, M.1995CAn E-M algorithm and testing strategy for multiple-locus haplotypes799-810Am J Hum Genet563*Algorithms Alleles Chromosome Mapping Data Interpretation, Statistical Genotype *Haplotypes Humans Linkage Disequilibrium *Models, Genetic Phenotype Research Support, U.S. Gov't, Non-P.H.S.MarVThis paper gives an expectation maximization (EM) algorithm to obtain allele frequencies, haplotype frequencies, and gametic disequilibrium coefficients for multiple-locus systems. It permits high polymorphism and null alleles at all loci. This approach effectively deals with the primary estimation problems associated with such systems; that is, there is not a one-to-one correspondence between phenotypic and genotypic categories, and sample sizes tend to be much smaller than the number of phenotypic categories. The EM method provides maximum-likelihood estimates and therefore allows hypothesis tests using likelihood ratio statistics that have chi 2 distributions with large sample sizes. We also suggest a data resampling approach to estimate test statistic sampling distributions. The resampling approach is more computer intensive, but it is applicable to all sample sizes. A strategy to test hypotheses about aggregate groups of gametic disequilibrium coefficients is recommended. This strategy minimizes the number of necessary hypothesis tests while at the same time describing the structure of disequilibrium. These methods are applied to three unlinked dinucleotide repeat loci in Navajo Indians and to three linked HLA loci in Gila River (Pima) Indians. The likelihood functions of both data sets are shown to be maximized by the EM estimates, and the testing strategy provides a useful description of the structure of gametic disequilibrium. Following these applications, a number of simulation experiments are performed to test how well the likelihood-ratio statistic distributions are approximated by chi 2 distributions. In most circumstances the chi 2 grossly underestimated the probability of type I errors. However, at times they also overestimated the type 1 error probability. Accordingly, we recommended hypothesis tests that use the resampling method.Journal Article<Laboratory of Neurogenetics, NIAAA/NIH, Rockville, MD 20852. ?GSchaid, D. J. Rowland, C. M. Tines, D. E. Jacobson, R. M. Poland, G. A.2002YScore tests for association between traits and haplotypes when linkage phase is ambiguous425-34Am J Hum Genet702Algorithms Case-Control Studies Chi-Square Distribution Chromosome Mapping/*methods/*statistics & numerical data Computer Simulation Genetic Predisposition to Disease/genetics HLA Antigens/genetics/immunology Haplotypes/*genetics Human Linkage (Genetics)/*genetics Measles Vaccine/immunology Quantitative Trait, Heritable Reproducibility of Results Research Design Software Support, U.S. Gov't, P.H.S.FebA key step toward the discovery of a gene related to a trait is the finding of an association between the trait and one or more haplotypes. Haplotype analyses can also provide critical information regarding the function of a gene; however, when unrelated subjects are sampled, haplotypes are often ambiguous because of unknown linkage phase of the measured sites along a chromosome. A popular method of accounting for this ambiguity in case-control studies uses a likelihood that depends on haplotype frequencies, so that the haplotype frequencies can be compared between the cases and controls; however, this traditional method is limited to a binary trait (case vs. control), and it does not provide a method of testing the statistical significance of specific haplotypes. To address these limitations, we developed new methods of testing the statistical association between haplotypes and a wide variety of traits, including binary, ordinal, and quantitative traits. Our methods allow adjustment for nongenetic covariates, which may be critical when analyzing genetically complex traits. Furthermore, our methods provide several different global tests for association, as well as haplotype-specific tests, which give a meaningful advantage in attempts to understand the roles of many different haplotypes. The statistics can be computed rapidly, making it feasible to evaluate the associations between many haplotypes and a trait. To illustrate the use of our new methods, they are applied to a study of the association of haplotypes (composed of genes from the human-leukocyte-antigen complex) with humoral immune response to measles vaccination. Limited simulations are also presented to demonstrate the validity of our methods, as well as to provide guidelines on how our methods could be used.HC, HK,Journal ArticleiDepartment of Health Sciences Research, Mayo Clinic/Foundation, Rochester, MN 55905, USA. schaid@mayo.edu? FSham, P. C. Rijsdijk, F. V. Knight, J. Makoff, A. North, B. Curtis, D.2004cHaplotype association analysis of discrete and continuous traits using mixture of regression models207-14 Behav Genet342Chromosome Mapping/*statistics & numerical data Epilepsy, Generalized/*genetics Gene Frequency/genetics Genetic Markers/genetics Genetics, Population *Genotype Haplotypes/*genetics Humans Logistic Models Mathematical Computing *Models, Genetic *Models, Statistical Phenotype Probability Quantitative Trait Loci/*genetics Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. SoftwareMarWe present a regression-based method of haplotype association analysis for quantitative and dichotomous traits in samples consisting of unrelated individuals. The method takes account of uncertain phase by initially estimating haplotype frequencies and obtaining the posterior probabilities of all possible haplotype combinations in each individual, then using these as weights in a finite mixture of regression models. Using this method, different combinations of marker loci can be modeled, to find a parsimonious set of marker loci that are most predictive and therefore most likely to be closely associated with the a quantitative trait locus. The method has the additional advantage of being able to use individuals with some missing genotype data, by considering all possible genotypes at the missing markers. We have implemented this method using the SNPHAP and Mx programs and illustrated its use on published data on idiopathic generalized epilepsy.PDF, HCJournal ArticleSocial, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, P.O. 080, King's College London, De Crespigny Park, London SE5 8AF, United Kingdom. p.sham@iop.kcl.ac.ukv? Stram, D. O.2004)Tag SNP selection for association studies365-74Genet Epidemiol274DecThis report describes current methods for selection of informative single nucleotide polymorphisms (SNPs) using data from a dense network of SNPs that have been genotyped in a relatively small panel of subjects. We discuss the following issues: (1) Optimal selection of SNPs based upon maximizing either the predictability of unmeasured SNPs or the predictability of SNP haplotypes as selection criteria. (2) The dependence of the performance of tag SNP selection methods upon the density of SNP markers genotyped for the purpose of haplotype discovery and tag SNP selection. (3) The likely power of case-control studies to detect the influence upon disease risk of common disease-causing variants in candidate genes in a haplotype-based analysis. We propose a quasi-empirical approach towards evaluating the power of large studies with this calculation based upon the SNP genotype and haplotype frequencies estimated in a haplotype discovery panel. In this calculation, each common SNP in turn is treated as a potential unmeasured causal variant and subjected to a correlation analysis using the remaining SNPs. We use a small portion of the HapMap ENCODE data (488 common SNPs genotyped over approximately a 500 kb region of chromosome 2) as an illustrative example of this approach towards power evaluation.HCJournal ArticleDivision of Biostatistics and Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA. stram@usc.edu Z? Stram, D. O. Leigh Pearce, C. Bretsky, P. Freedman, M. Hirschhorn, J. N. Altshuler, D. Kolonel, L. N. Henderson, B. E. Thomas, D. C.2003Modeling and E-M estimation of haplotype-specific relative risks from genotype data for a case-control study of unrelated individuals179-90 Hum Hered554{Algorithms Breast Neoplasms/*ethnology/*genetics Case-Control Studies Cohort Studies Comparative Study Computer Simulation Female Genetic Predisposition to Disease Genotype Haplotypes/*genetics Humans Incidence Likelihood Functions *Models, Genetic Polymorphism, Single Nucleotide/*genetics Research Support, U.S. Gov't, P.H.S. Risk Factors Steroid 17-alpha-Hydroxylase/*geneticsThe US National Cancer Institute has recently sponsored the formation of a Cohort Consortium (http://2002.cancer.gov/scpgenes.htm) to facilitate the pooling of data on very large numbers of people, concerning the effects of genes and environment on cancer incidence. One likely goal of these efforts will be generate a large population-based case-control series for which a number of candidate genes will be investigated using SNP haplotype as well as genotype analysis. The goal of this paper is to outline the issues involved in choosing a method of estimating haplotype-specific risk estimates for such data that is technically appropriate and yet attractive to epidemiologists who are already comfortable with odds ratios and logistic regression. Our interest is to develop and evaluate extensions of methods, based on haplotype imputation, that have been recently described (Schaid et al., Am J Hum Genet, 2002, and Zaykin et al., Hum Hered, 2002) as providing score tests of the null hypothesis of no effect of SNP haplotypes upon risk, which may be used for more complex tasks, such as providing confidence intervals, and tests of equivalence of haplotype-specific risks in two or more separate populations. In order to do so we (1) develop a cohort approach towards odds ratio analysis by expanding the E-M algorithm to provide maximum likelihood estimates of haplotype-specific odds ratios as well as genotype frequencies; (2) show how to correct the cohort approach, to give essentially unbiased estimates for population-based or nested case-control studies by incorporating the probability of selection as a case or control into the likelihood, based on a simplified model of case and control selection, and (3) finally, in an example data set (CYP17 and breast cancer, from the Multiethnic Cohort Study) we compare likelihood-based confidence interval estimates from the two methods with each other, and with the use of the single-imputation approach of Zaykin et al. applied under both null and alternative hypotheses. We conclude that so long as haplotypes are well predicted by SNP genotypes (we use the Rh2 criteria of Stram et al. [1]) the differences between the three methods are very small and in particular that the single imputation method may be expected to work extremely well.Journal ArticleDepartment of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA. stram@usc.edu w? +Templeton, A. R. Boerwinkle, E. Sing, C. F.1987A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping. I. Basic theory and an analysis of alcohol dehydrogenase activity in Drosophila343-51Genetics1172Alcohol Dehydrogenase/*genetics Animals DNA Restriction Enzymes Drosophila melanogaster/enzymology/*genetics *Genes *Haplotypes *Models, Genetic Nucleotide Mapping Phenotype Research Support, U.S. Gov't, P.H.S.OctBecause some genes have been cloned that have a known biochemical or physiological function, genetic variation can be measured in a population at loci that may directly influence a phenotype of interest. With this measured genotype approach, specific alleles or haplotypes in the probed DNA region can be assigned phenotypic effects. In this paper we address several problems encountered in implementing the measured genotype approach with restriction site data. A number of analytical problems arise in part as a consequence of the linkage disequilibrium that is commonly encountered when dealing with small DNA regions: 1) different restriction site polymorphisms are not statistically independent, 2) the sites being measured are not likely to be the direct cause of the associated phenotypic effects, 3) haplotype classes may be phenotypically heterogeneous, and 4) the sites that are most strongly associated with phenotypic effects are not necessarily the most closely linked to the actual genetic cause of the effects. When recombination and gene conversion are rare, the primary cause of linkage disequilibrium is history (mutational origin, genetic drift, hitchhiking, etc.). We deal with historical association directly by producing a cladogram that partially reconstructs the evolutionary history of the present-day haplotype variability. The cladogram defines a nested analysis of variance that simultaneously detects phenotypic effects, localizes the effects within the cladogram, and identifies haplotypes that are potentially heterogeneous in their phenotypic associations. The power of this approach is illustrated by an analysis of the associations between alcohol dehydrogenase (ADH) activity and restriction site variability in a 13-kb fragment surrounding the ADH locus in Drosophila melanogaster.Journal ArticleKDepartment of Human Genetics, University of Michigan, Ann Arbor 48109-0618.? Xie, X. Ott, J.1993ETesting linkage disequilibrium between a disease gene and marker loci1107Am J Hum Genet53. }?!Zaykin, D. V. Meng, Z. Ehm, M. G.2006lContrasting linkage-disequilibrium patterns between cases and controls as a novel association-mapping method737-46Am J Hum Genet785sCase-Control Studies Chromosome Mapping/ methods Computational Biology/methods/statistics & numerical data Computer Simulation Cytochrome P-450 CYP2D6/ genetics/pharmacology Genetic Markers Genetic Predisposition to Disease Haplotypes Humans Linkage Disequilibrium Models, Statistical Polymorphism, Single Nucleotide Quantitative Trait, Heritable Variation (Genetics)MayIdentification and description of genetic variation underlying disease susceptibility, efficacy, and adverse reactions to drugs remains a difficult problem. One of the important steps in the analysis of variation in a candidate region is the characterization of linkage disequilibrium (LD). In a region of genetic association, the extent of LD varies between the case and the control groups. Separate plots of pairwise standardized measures of LD (e.g., D') for cases and controls are often presented for a candidate region, to graphically convey case-control differences in LD. However, the observed graphic differences lack statistical support. Therefore, we suggest the "LD contrast" test to compare whole matrices of disequilibrium between two samples. A common technique of assessing LD when the haplotype phase is unobserved is the expectation-maximization algorithm, with the likelihood incorporating the assumption of Hardy-Weinberg equilibrium (HWE). This approach presents a potential problem in that, in the region of genetic association, the HWE assumption may not hold when samples are selected on the basis of phenotypes. Here, we present a computationally feasible approach that does not assume HWE, along with graphic displays and a statistical comparison of pairwise matrices of LD between case and control samples. LD-contrast tests provide a useful addition to existing tools of finding and characterizing genetic associations. Although haplotype association tests are expected to provide superior power when susceptibilities are primarily determined by haplotypes, the LD-contrast tests demonstrate substantially higher power under certain haplotype-driven disease models.0002-9297 (Print)16642430National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA. zaykind@niehs.nih.gov?3Zhao, J. H. Lissarrague, S. Essioux, L. Sham, P. C.20027GENECOUNTING: haplotype analysis with missing genotypes1694-5Bioinformatics1812*Algorithms Alleles *Gene Frequency Genetic Markers/genetics *Genotype Haplotypes/*genetics Likelihood Functions Sequence Analysis, DNA/*methods Software Support, Non-U.S. Gov'tDecA general algorithm is described for haplotype analysis of unrelated individuals with missing genotypes. It can handle problems involving multiple polymorphic markers with missing data.HCJournal ArticleDepartment of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK. j.zhao@public-health.ucl.ac.ukV~?Nance, W. E. Neale, M. C.1989(Partitioned twin analysis: a power study143-50 Behav Genet191*Computer Simulation DNA/*genetics Humans *Models, Genetic *Polymorphism, Genetic *Polymorphism, Restriction Fragment Length *Software *TwinsJanhIndividual differences in the human genome may now be measured with molecular genetic techniques. Therefore, dizygotic (DZ) twins may be classified as sharing two, one, or zero "genes" identical by descent for any measured polymorphism. As a result, we may partition genetic variation into two sources: (i) genotypes at and closely linked to particular marker loci identified with restriction fragment length polymorphisms (RFLPs) and (ii) other genetic variation. The power of the classical twin study to reject false models lacking either a marker effect or a residual genetic effect is explored. Additivity of genetic effects at or near the locus and of the residual genetic variation as well as random environmental variation are assumed. Results indicate that statistical rejection of models could be achieved with sample sizes which are within the range of several current twin registers. A design including monozygotic (MZ) twins is compared with one consisting of only DZ twins. MZ twins add considerable power for the detection of residual genetic variation but provide no information to resolve genetic marker effects.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2565716 g0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.2565716~? Purcell, S.2002LVariance components models for gene-environment interaction in twin analysis554-71Twin Res56]Computer Simulation *Environment Genetics, Behavioral Humans *Models, Genetic Twins/*geneticsDec]Gene-environment interaction is likely to be a common and important source of variation for complex behavioral traits. Often conceptualized as the genetic control of sensitivity to the environment, it can be incorporated in variance components twin analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. The model allows for one or more environmental moderator variables (that possibly interact with each other) that may i). be continuous or binary ii). differ between twins within a pair iii). interact with residual environmental as well as genetic effects iv) have nonlinear moderating properties v). show scalar (different magnitudes) or qualitative (different genes) interactions vi). be correlated with genetic effects acting upon the trait, to allow for a test of gene-environment interaction in the presence of gene-environment correlation. Aspects and applications of a class of models are explored by simulation, in the context of both individual differences twin analysis and, in a companion paper (Purcell & Sham, 2002) sibpair quantitative trait locus linkage analysis. As well as elucidating environmental pathways, consideration of gene-environment interaction in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12573187 r1369-0523 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Twin Study12573187Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College, London, UK. s.purcell@iop.kcl.ac.ukyO?Strachan, T. Read, A1999Human Molecular Genetics 2OxfordBIOS Scientific Publishers Ltd2nd~?(Olson, J. M. Witte, J. S. Elston, R. C. 1999!Genetic mapping of complex traits 2961-2981 Stat Med 18~?(Plomin, R. DeFries, J. C. Loehlin, J. C.1977RGenotype-environment interaction and correlation in the analysis of human behavior309-22 Psychol Bull842oAdoption Child *Child Behavior Female Genetics, Behavioral *Genotype Humans Pregnancy *Social Environment TwinsMardhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=557211 !0033-2909 (Print) Journal Article557211~?)Fulker, D. W. Cherny, S. S. Cardon, L. R.1995GMultipoint interval mapping of quantitative trait loci, using sib pairs1224-33Am J Hum Genet565Alleles Chromosome Mapping/*methods *Computer Simulation Genetic Markers Humans *Models, Genetic *Nuclear Family Statistics/methods Time FactorsMayThe sib-pair interval-mapping procedure of Fulker and Cardon is extended to take account of all available marker information on a chromosome simultaneously. The method provides a computationally fast multipoint analysis of sib-pair data, using a modified Haseman-Elston approach. It gives results very similar to those of the earlier interval-mapping procedure when marker information is relatively uniform and a coarse map is used. However, there is a substantial improvement over the original method when markers differ in information content and/or when a dense map is employed. The method is illustrated by using simulated sib-pair data.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7726180 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.7726180SInstitute for Behavioral Genetics, University of Colorado, Boulder 80309-0447, USA.~? Feingold, E.2002ERegression-based quantitative-trait-locus mapping in the 21st century217-22Am J Hum Genet712Animals Chromosome Mapping/statistics & numerical data/*trends Forecasting Humans *Likelihood Functions Normal Distribution *Quantitative Trait, Heritable *Regression AnalysisAugfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12154779 H0002-9297 (Print) Comment Editorial Research Support, U.S. Gov't, P.H.S.12154779 ~?+Holliday, E. Mowry, B. Chant, D. Nyholt, D.2005xThe importance of modelling heterogeneity in complex disease: application to NIMH Schizophrenia Genetics Initiative data160-7 Hum Genet1172-3Chromosomes, Human, Pair 10/*genetics Chromosomes, Human, Pair 15/*genetics Databases, Genetic *Genetic Predisposition to Disease Genotype Humans *Lod Score Schizophrenia/*geneticsJulAs for other complex diseases, linkage analyses of schizophrenia (SZ) have produced evidence for numerous chromosomal regions, with inconsistent results reported across studies. The presence of locus heterogeneity appears likely and may reduce the power of linkage analyses if homogeneity is assumed. In addition, when multiple heterogeneous datasets are pooled, inter-sample variation in the proportion of linked families (alpha) may diminish the power of the pooled sample to detect susceptibility loci, in spite of the larger sample size obtained. We compare the significance of linkage findings obtained using allele-sharing LOD scores (LOD(exp))-which assume homogeneity-and heterogeneity LOD scores (HLOD) in European American and African American NIMH SZ families. We also pool these two samples and evaluate the relative power of the LOD(exp) and two different heterogeneity statistics. One of these (HLOD-P) estimates the heterogeneity parameter alpha only in aggregate data, while the second (HLOD-S) determines alpha separately for each sample. In separate and combined data, we show consistently improved performance of HLOD scores over LOD(exp). Notably, genome-wide significant evidence for linkage is obtained at chromosome 10p in the European American sample using a recessive HLOD score. When the two samples are combined, linkage at the 10p locus also achieves genome-wide significance under HLOD-S, but not HLOD-P. Using HLOD-S, improved evidence for linkage was also obtained for a previously reported region on chromosome 15q. In linkage analyses of complex disease, power may be maximised by routinely modelling locus heterogeneity within individual datasets, even when multiple datasets are combined to form larger samples.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15843988 30340-6717 (Print) Journal Article Multicenter Study15843988Queensland Centre for Mental Health Research, Level 3, Dawson House, The Park, Centre for Mental Health, Wacol, QLD 4076, Australia. lizh@qcmhr.uq.edu.au~?SAbecasis, G. Cox, N. Daly, M. J. Kruglyak, L. Laird, N. Markianos, K. Patterson, N.2004No bias in linkage analysis722-3; author reply 723-7Am J Hum Genet754*Bias (Epidemiology) Chromosome Mapping/*methods Data Interpretation, Statistical Likelihood Functions Lod Score *Models, Genetic *Research Design SiblingsOctfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15338460 0002-9297 (Print) Comment Letter15338460.~?Williams, J. T. Blangero, J.2004@Power of variance component linkage analysis-II. Discrete traits620-32 Ann Hum Genet68Pt 6Analysis of Variance Computer Simulation Data Interpretation, Statistical *Genetics, Population Humans *Linkage (Genetics) Quantitative Trait LociNovWe determine the power of variance component linkage analysis in the case of discrete, dichotomous traits analyzed under a classical liability threshold model. For simplicity we consider randomly ascertained samples and an additive model of variation incorporating a qtl, residual additive genetic factors, and individual-specific random environmental effects. We derive an expression for the power of variance component linkage analysis in arbitrary relative pairs, and compare the power of discrete and quantitative trait linkage analysis in the specific case of sibpairs. The predicted sample sizes required in linkage analysis of sibpairs are confirmed by analysis of simulated data. Unlike the affected-sibpair method, the power of discrete trait variance component analysis increases with trait prevalence. The relative efficiency of a discrete trait for linkage analysis increases with population trait prevalence, but does not exceed about 40% and is typically much less.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15598220 F0003-4800 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.15598220dSouthwest Foundation for Biomedical Research, San Antonio, TX 78245-0549, USA. jeffw@darwin.sfbr.org;~?Nicolae, D. L. Kong, A.2004DMeasuring the relative information in allele-sharing linkage studies368-75 Biometrics602V*Alleles *Biometry Humans Likelihood Functions *Linkage (Genetics) Models, StatisticalJunIn the context of allele-sharing methods, this article investigates ways of measuring the information in the marker data relative to the amount of information that would have been available if the identity-by-descent (IBD) process were known. Such measures are needed to decide whether new markers can substantially modify the evidence for excess sharing. We propose new measures that take advantage of the properties of the exponential model introduced by Kong and Cox (1997, American Journal of Human Genetics61, 1179-1188). These measures are related to Fisher Information and hence are also efficiency measures. Large-sample and small-sample properties of the new and previously proposed measures of information are examined.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15180662 o0006-341X (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.15180662Department of Statistics, University of Chicago, 5734 S. University Avenue, Chicago, Illinois 60637, USA. nicolae@galton.uchicago.edu ~?Eaves, L. Meyer, J.1994dLocating human quantitative trait loci: guidelines for the selection of sibling pairs for genotyping443-55 Behav Genet245Alleles *Chromosome Mapping Genes, Dominant Genes, Recessive Genetic Markers/genetics *Genotype Humans Models, Genetic Phenotype *Selection (Genetics)SepXSimulation studies were conducted to assess the relative merits of different nonrandom sampling strategies for the selection of sibling pairs for genotyping in the attempt to locate individual loci (QTLs) contributing to variation in human quantitative traits. For a constant amount of variation contributed by a QTL (25% of the total) the frequencies and dominance relationships of a trait increasing allele were varied. Three strategies for selection of pairs for genotyping were based on the phenotypic values of the siblings: "Concordant sib pairs" (CSP) are pairs in which both individuals exceed a given threshold value; "discordant sib pairs" (DSP) are pairs in which one member exceeds a given upper threshold and the other is below a specified lower threshold; and "most similar pairs" (MSP) are pairs selected for falling below a specified percentile ranking of the within-pair mean square for the quantitative trait. Tests for linkage with markers at 1, 2, 5, 10, and 20 cM from each of the QTLs were conducted for each of the selected samples and compared with tests based on the regression, in the entire sample, of within pair variation on the proportion of alleles identical by descent (IBD) at each marker locus. Tests for the effect of the increasing allele at the QTL ("candidate gene") were also conducted for the DSP pairs. No single nonrandom selection procedure yields as much as half the information realized in the total sample. However, a combined strategy which involves genotyping the 5% of MSP and DSP for the upper and lower quintiles of values of the quantitative trait (a further 3% of the sample approximately) yields lod scores which are usually more than 65% of the values realized for the entire sample. Tests comparing the proportion of increasing alleles in high- and low-scoring siblings from DSP samples are uniformly very powerful for detecting candidate loci. Even when it is not possible to measure the entire range of the phenotype with uniform precision, some attempt to differentiate among individuals in a common "unaffected" class of individuals can lead to considerable increase in power.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7993321 !0001-8244 (Print) Journal Article7993321TDepartment of Human Genetics, Virginia Commonwealth University, Richmond 23298-0003. Y~?Chen, W. M. Abecasis, G. R.2006NEstimating the power of variance component linkage analysis in large pedigrees471-84Genet Epidemiol306*Chromosome Mapping Computer Simulation Female Humans *Likelihood Functions *Linkage (Genetics) Male Models, Genetic Models, Statistical Pedigree Phenotype *Quantitative Trait LociSepVariance component linkage analysis is commonly used to map quantitative trait loci (QTLs) in general pedigrees. Large pedigrees are especially attractive for these studies because they provide greater power per genotyped individual than small pedigrees. We propose accurate and computationally efficient methods to calculate the analytical power of variance component linkage analysis that can accommodate large pedigrees. Our analytical power computation involves the approximation of the noncentrality parameter for the likelihood-ratio test by its Taylor expansions. We develop efficient algorithms to compute the second and third moments of the identical by descent (IBD) sharing distribution and enable rapid computation of the Taylor expansions. Our algorithms take advantage of natural symmetries in pedigrees and can accurately analyze many large pedigrees in a few seconds. We verify the accuracy of our power calculation via simulation in pedigrees with 2-5 generations and 2-8 siblings per sibship. We apply this proposed analytical power calculation to 98 quantitative traits in a cohort study of 6,148 Sardinians in which the largest pedigree includes 625 phenotyped individuals. Simulations based on eight representative traits show that the difference between our analytical estimation of the expected LOD score and the average of simulated LOD scores is less than 0.05 (1.5%). Although our analytical calculations are for a fully informative marker locus, in the settings we examined power was similar to what could be attained with a single nucleotide polymorphism (SNP) mapping panel (with >1 SNP/cM). Our algorithms for power analysis together with polygenic analysis are implemented in a freely available computer program, POLY.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16685720 F0741-0395 (Print) Journal Article Research Support, N.I.H., Extramural16685720_Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA. wechen@umich.edub?Box, G Cox, D.1964An analysis of transformations.211-252 J R Sat Soc26?4Abecasis, G.R. Cherny, S.S. Cookson, W.O Cardon, L.R UnpublishedCMinx (http://www.sph.umich.edu/csg/abecasis/Merlin/reference.html).Z? Falconer, D.S1960Quantitative Genetics EdinburghOliver and Boyd7? Ahmadi, K. R. Weale, M. E. Xue, Z. Y. Soranzo, N. Yarnall, D. P. Briley, J. D. Maruyama, Y. Kobayashi, M. Wood, N. W. Spurr, N. K. Burns, D. K. Roses, A. D. Saunders, A. M. Goldstein, D. B.2005TA single-nucleotide polymorphism tagging set for human drug metabolism and transport84-9 Nat Genet371Janehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1560864015608640vDepartment of Biology (Galton Lab), University College London, The Darwin Building, Gower Street, London WC1E 6BT, UK.?!OCarlson, C. S. Eberle, M. A. Rieder, M. J. Yi, Q. Kruglyak, L. Nickerson, D. A.2004~Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium106-20Am J Hum Genet741African Continental Ancestry Group Algorithms European Continental Ancestry Group Genetic Diseases, Inborn/*genetics Homozygote Humans Linkage Disequilibrium/*genetics Polymorphism, Single Nucleotide/*genetics United StatesJanfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14681826 14681826hDepartment of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. csc47@u.washington.edu{?" Geiringer, H1944:On the probability theory of linkage in Mendelian heredity25-57 Ann Math Stat15b?#Mueller, J. C. Lohmussaar, E. Magi, R. Remm, M. Bettecken, T. Lichtner, P. Biskup, S. Illig, T. Pfeufer, A. Luedemann, J. Schreiber, S. Pramstaller, P. Pichler, I. Romeo, G. Gaddi, A. Testa, A. Wichmann, H. E. Metspalu, A. Meitinger, T.2005ULinkage disequilibrium patterns and tagSNP transferability among European populations387-98Am J Hum Genet763Marehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1563765915637659jInstitute of Human Genetics, GSF-National Research Centre for Environment and Health, Neuherberg, Germany.N~?$Nejentsev, S. Godfrey, L. Snook, H. Rance, H. Nutland, S. Walker, N. M. Lam, A. C. Guja, C. Ionescu-Tirgoviste, C. Undlien, D. E. Ronningen, K. S. Tuomilehto-Wolf, E. Tuomilehto, J. Newport, M. J. Clayton, D. G. Todd, J. A.2004Comparative high-resolution analysis of linkage disequilibrium and tag single nucleotide polymorphisms between populations in the vitamin D receptor gene1633-9 Hum Mol Genet1315*Chromosomes, Human, Pair 12 Gambia Great Britain Humans *Linkage Disequilibrium *Polymorphism, Single Nucleotide Receptors, Calcitriol/*geneticsAug 1A genome-wide map of single nucleotide polymorphisms (SNP) and a pattern of linkage disequilibrium (LD) between their alleles are being established in three main ethnic groups. An important question is the applicability of such maps to different populations within a main ethnic group. Therefore, we have developed high-resolution SNP, haplotype and LD maps of vitamin D receptor gene region in large samples from five populations. Comparative analysis reveals that the LD patterns are identical in all four European populations tested with two small regions of 1.3 and 5.7 kb at which LD is disrupted completely resulting in three block-like regions over which there is significant and extensive LD. In an African population the pattern is similar, but two additional LD-breaking spots are also apparent. This LD pattern suggests combined action of recombination hotspots and founder effects, but cannot be explained by random recombination and genetic drift alone. Direct comparison indicates that the tag SNPs selected in one European population effectively predict the non-tag SNPs in the other Europeans, but not in the Gambians, for this region.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15175274 B0964-6906 (Print) Journal Article Research Support, Non-U.S. Gov't15175274Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, UK. sergey.nejentsev@cimr.cam.ac.uk :~?%FTapper, W. Collins, A. Gibson, J. Maniatis, N. Ennis, S. Morton, N. E.20059A map of the human genome in linkage disequilibrium units11835-9Proc Natl Acad Sci U S A10233Chromosome Mapping Chromosomes, Human, Pair 19/genetics Female *Genome, Human Humans Linkage Disequilibrium/*genetics Male Polymorphism, Single Nucleotide/genetics Recombination, Genetic/genetics Selection (Genetics) Sex Characteristics Time FactorsAug 16Two genetic maps with additive distances contribute information about recombination patterns, recombinogenic sequences, and discovery of genes affecting a particular phenotype. Recombination is measured in morgans (w) over a single generation in a linkage map but may cover thousands of generations in a linkage disequilibrium (LD) map measured in LD units (LDU). We used a subset of single nucleotide polymorphisms from the HapMap Project to create a genome-wide map in LDU. Recombination accounts for 96.8% of the LDU variance in chromosome arms and 92.4% in their deciles. However, deeper analysis shows that LDU/w, an estimate of the effective bottleneck time (t), is significantly variable among chromosome arms because (i) the linkage map is approximated from the Haldane function, then adjusted toward the Kosambi function that is more accurate but still exaggerates w for all chromosomes, especially shorter ones; (ii) the non-pseudoautosomal region of the X chromosome is subject to hemizygous selection; and (iii) at resolution less than approximately 40,000 markers per w, there are indeterminacies (holes) in the LD map reflecting intervals of very high recombination. Selection and stochastic variation in small regions must have effects, which remain to be investigated by comparisons among populations. These considerations suggest an optimal strategy to eliminate holes quickly, greatly enhance the resolution of sex-specific linkage maps, and maximize the gain in association mapping by using LD maps.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16091463 0027-8424 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.16091463wHuman Genetics Division, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, United Kingdom.k~?&[Lake, S. L. Lyon, H. Tantisira, K. Silverman, E. K. Weiss, S. T. Laird, N. M. Schaid, D. J.2003YEstimation and tests of haplotype-environment interaction when linkage phase is ambiguous56-65 Hum Hered551Algorithms Anti-Inflammatory Agents/therapeutic use Asthma/drug therapy/epidemiology/*genetics Chromosome Mapping/methods/statistics & numerical data Computer Simulation Environment Genetic Markers Genetic Predisposition to Disease/genetics Haplotypes/*genetics Humans Linkage (Genetics)/*genetics Models, Genetic Polymorphism, Single Nucleotide/*genetics Quantitative Trait, Heritable *SmokingIn the study of complex traits, the utility of linkage analysis and single marker association tests can be limited for researchers attempting to elucidate the complex interplay between a gene and environmental covariates. For these purposes, tests of gene-environment interactions are needed. In addition, recent studies have indicated that haplotypes, which are specific combinations of nucleotides on the same chromosome, may be more suitable as the unit of analysis for statistical tests than single genetic markers. The difficulty with this approach is that, in standard laboratory genotyping, haplotypes are often not directly observable. Instead, unphased marker phenotypes are collected. In this article, we present a method for estimating and testing haplotype-environment interactions when linkage phase is potentially ambiguous. The method builds on the work of Schaid et al. [2002] and is applicable to any trait that can be placed in the generalized linear model framework. Simulations were run to illustrate the salient features of the method. In addition, the method was used to test for haplotype-smoking exposure interaction with data from the Childhood Asthma Management Program.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12890927 F0001-5652 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.12890927Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass. 02115, USA. Stephen.Lake@channing.harvard.edu?*'Gordon, D Finch, SJ Nothnagel, M Ott, J2002Power and sample size calculations for case-control genetic association tests when errors are present: application to single nucleotide polymorphisms.22-33Human Heredity 54?+ Mitra, SK1958?On the limiting power function of the frequency chi-square test 1221-1233!Annals of Mathematical Statistics29~?,#Devlin, B. Roeder, K. Wasserman, L.2000aGenomic control for association studies: a semiparametric test to detect excess-haplotype sharing369-87 Biostatistics14DecIndividuals who share a disease mutation from a common ancestor often share alleles at genetic markers adjacent to the mutation, even if the common ancestor is remote. The alleles at these adjacent markers, called the haplotype, can be visualized as a string of realizations of random variables, which may be dependent when individuals are related in some fashion. Ideally, for a sample of individuals all having the same (genetic) disease, this dependence-measured as haplotype-sharing-will be greater in the vicinity of disease genes than in other regions of the genome. In this paper we present a semiparametric test for haplotype-sharing. We begin by developing a model assuming that the ancestral haplotype is known and thus the extent of haplotype-sharing from a common ancestor can be determined unambiguously. The amount of overlap at markers far from the disease is treated as a random variable with an unknown distribution F, which we estimate non-parametrically. Overlap of markers surrounding disease genes are modeled as a mixture pF(x - theta) + (1 - p)F(x), in which p is the fraction of subjects with the disease mutation. Testing for a disease gene then amounts to testing whether p = 0. Next we drop the assumption that the ancestral haplotype is known. To detect excess clustering of haplotypes, we measure the pairwise overlap of a set of haplotypes. As in the simpler scenario, this distribution is modeled as a location-shift mixture. To test the hypothesis we construct a score test with a simple limiting distribution.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12933562 !1465-4644 (Print) Journal Article12933562NDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA.?-Johnson, R.A Wichern, D.W2002)Applied Multivariate Statistical AnalysisUpper Saddle River, NJ.Prentice Hall,?. Kirk, R.E. 1994HExperimental Design: Procedures for the Behavioral Sciences (Psychology) Belmont, CA.Wadsworth Publishing?/Moore, D.S. McCabe, G.P1999+Introduction to the Practice of Statistics. New York, NY.W.H. Freeman and Companyt?0Tabachnik, B.G. Fidell, L.S.2001Using Multivariate Statistics Boston, MA.Allyn and Baconn?1Ash, C.1993.The Probability Tutoring Book Revised PrintingPiscataway, NJ. IEEE Pressm?2Gonick, L. Smith, M.1994Cartoon Guide to Statistics. New York, NY.Harper Collins,o?3 Searle, S.R.1982$Matrix Algebra Useful for Statistics New York, NY.Wiley-Interscience?4 Edwards, A.L.19765An Introduction to Linear Regression and Correlation. San Francisco, CA. W.H. Freeman,?5 Edwards, A.L.1979@Multiple Regression and the Analysis of Variance and Covariance. San Francisco, CA. W.H. Freemanv?6 Agresti, A.2007,An Introduction to Categorical Data Analysis New York, NY.Wiley-Interscience~?7Purcell, S. Neale, B. Todd-Brown, K. Thomas, L. Ferreira, M. A. Bender, D. Maller, J. Sklar, P. de Bakker, P. I. Daly, M. J. Sham, P. C.2007TPLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses559-75Am J Hum Genet813SepWhole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17701901 !0002-9297 (Print) Journal Article17701901vCenter for Human Genetic Research, Massachusetts General Hospital, Boston, MA, 02114, USA. shaun@pngu.mgh.harvard.edu.i~?8$Frayling, T. M. Timpson, N. J. Weedon, M. N. Zeggini, E. Freathy, R. M. Lindgren, C. M. Perry, J. R. Elliott, K. S. Lango, H. Rayner, N. W. Shields, B. Harries, L. W. Barrett, J. C. Ellard, S. Groves, C. J. Knight, B. Patch, A. M. Ness, A. R. Ebrahim, S. Lawlor, D. A. Ring, S. M. Ben-Shlomo, Y. Jarvelin, M. R. Sovio, U. Bennett, A. J. Melzer, D. Ferrucci, L. Loos, R. J. Barroso, I. Wareham, N. J. Karpe, F. Owen, K. R. Cardon, L. R. Walker, M. Hitman, G. A. Palmer, C. N. Doney, A. S. Morris, A. D. Smith, G. D. Hattersley, A. T. McCarthy, M. I.2007rA common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity889-94Science3165826JAdipose Tissue Adolescent Adult Aged Alleles Birth Weight *Body Mass Index Case-Control Studies Child Cohort Studies Diabetes Mellitus, Type 2/*genetics Female *Genetic Predisposition to Disease Great Britain Homozygote Humans Infant, Newborn Male Middle Aged Obesity/*genetics Overweight/genetics *Polymorphism, Single NucleotideMay 11Obesity is a serious international health problem that increases the risk of several common diseases. The genetic factors predisposing to obesity are poorly understood. A genome-wide search for type 2 diabetes-susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI). An additive association of the variant with BMI was replicated in 13 cohorts with 38,759 participants. The 16% of adults who are homozygous for the risk allele weighed about 3 kilograms more and had 1.67-fold increased odds of obesity when compared with those not inheriting a risk allele. This association was observed from age 7 years upward and reflects a specific increase in fat mass.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17434869 G1095-9203 (Electronic) Journal Article Research Support, Non-U.S. Gov't17434869~Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Magdalen Road, Exeter, UK.~?94de Andrade, M. Fridley, B. Boerwinkle, E. Turner, S.2003<Diagnostic tools in linkage analysis for quantitative traits302-8Genet Epidemiol244Coronary Arteriosclerosis/genetics Data Interpretation, Statistical Female Humans Hypertension/genetics *Linkage (Genetics) Male Methods Models, Genetic Multifactorial Inheritance Normal Distribution Pedigree *Quantitative Trait, Heritable Triglycerides/analysis/geneticsMay!Diagnostic methods are key components in any good statistical analysis. Because of the similarities between the variance components approach and regression analysis with respect to the normality assumption, when performing quantitative genetic linkage analysis using variance component methods, one must check the normality assumption of the quantitative trait and outliers. Thus, the main purposes of this paper are to describe methods for testing the normality assumption, to describe various diagnostic methods for identifying outliers, and to discuss the issues that may arise when outliers are present when using variance components models in quantitative trait linkage analysis. Data from the Rochester Family Heart Study are used to illustrate the various diagnostic methods and related issues.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12687648 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.12687648gDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA. mandrade@mayo.edu ~?:Gonzalez-Neira, A. Ke, X. Lao, O. Calafell, F. Navarro, A. Comas, D. Cann, H. Bumpstead, S. Ghori, J. Hunt, S. Deloukas, P. Dunham, I. Cardon, L. R. Bertranpetit, J.2006AThe portability of tagSNPs across populations: a worldwide survey323-30 Genome Res163Chromosomes, Human, Pair 22/genetics Data Collection/*methods Genetics, Population Haplotypes Humans Linkage Disequilibrium/genetics Polymorphism, Single Nucleotide/*genetics Variation (Genetics)MarIn the search for common genetic variants that contribute to prevalent human diseases, patterns of linkage disequilibrium (LD) among linked markers should be considered when selecting SNPs. Genotyping efficiency can be increased by choosing tagging SNPs (tagSNPs) in LD with other SNPs. However, it remains to be seen whether tagSNPs defined in one population efficiently capture LD in other populations; that is, how portable tagSNPs are. Indeed, tagSNP portability is a challenge for the applicability of HapMap results. We analyzed 144 SNPs in a 1-Mb region of chromosome 22 in 1055 individuals from 38 worldwide populations, classified into seven continental groups. We measured tagSNP portability by choosing three reference populations (to approximate the three HapMap populations), defining tagSNPs, and applying them to other populations independently on the availability of information on the tagSNPs in the compared population. We found that tagSNPs are highly informative in other populations within each continental group. Moreover, tagSNPs defined in Europeans are often efficient for Middle Eastern and Central/South Asian populations. TagSNPs defined in the three reference populations are also efficient for more distant and differentiated populations (Oceania, Americas), in which the impact of their special demographic history on the genetic structure does not interfere with successfully detecting the most common haplotype variation. This high degree of portability lends promise to the search for disease association in different populations, once tagSNPs are defined in a few reference populations like those analyzed in the HapMap initiative.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16467560 B1088-9051 (Print) Journal Article Research Support, Non-U.S. Gov't16467560Unitat de Biologia Evolutiva, Departament de Ciencies Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain.?; Baum, L.E.1972}An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes1-8 Inequalities3~?<Abecasis, G. R. Burt, R. A. Hall, D. Bochum, S. Doheny, K. F. Lundy, S. L. Torrington, M. Roos, J. L. Gogos, J. A. Karayiorgou, M.2004Genomewide scan in families with schizophrenia from the founder population of Afrikaners reveals evidence for linkage and uniparental disomy on chromosome 1403-17Am J Hum Genet743Chromosome Mapping *Chromosomes, Human, Pair 1 European Continental Ancestry Group/genetics Female *Founder Effect Humans Linkage (Genetics) Lod Score Male Pedigree Schizophrenia/*genetics South Africa/epidemiology Statistics, Nonparametric *Uniparental DisomyMardWe report on our initial genetic linkage studies of schizophrenia in the genetically isolated population of the Afrikaners from South Africa. A 10-cM genomewide scan was performed on 143 small families, 34 of which were informative for linkage. Using both nonparametric and parametric linkage analyses, we obtained evidence for a small number of disease loci on chromosomes 1, 9, and 13. These results suggest that few genes of substantial effect exist for schizophrenia in the Afrikaner population, consistent with our previous genealogical tracing studies. The locus on chromosome 1 reached genomewide significance levels (nonparametric LOD score of 3.30 at marker D1S1612, corresponding to an empirical P value of.012) and represents a novel susceptibility locus for schizophrenia. In addition to providing evidence for linkage for chromosome 1, we also identified a proband with a uniparental disomy (UPD) of the entire chromosome 1. This is the first time a UPD has been described in a patient with schizophrenia, lending further support to involvement of chromosome 1 in schizophrenia susceptibility in the Afrikaners.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14750073 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.14750073HDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, USA. A~?=,Abecasis, G. R. Cardon, L. R. Cookson, W. O.2000IA general test of association for quantitative traits in nuclear families279-92Am J Hum Genet661s*Genetics, Population Humans *Linkage Disequilibrium Models, Genetic *Nuclear Family *Quantitative Trait, HeritableJanHigh-resolution mapping is an important step in the identification of complex disease genes. In outbred populations, linkage disequilibrium is expected to operate over short distances and could provide a powerful fine-mapping tool. Here we build on recently developed methods for linkage-disequilibrium mapping of quantitative traits to construct a general approach that can accommodate nuclear families of any size, with or without parental information. Variance components are used to construct a test that utilizes information from all available offspring but that is not biased in the presence of linkage or familiality. A permutation test is described for situations in which maximum-likelihood estimates of the variance components are biased. Simulation studies are used to investigate power and error rates of this approach and to highlight situations in which violations of multivariate normality assumptions warrant the permutation test. The relationship between power and the level of linkage disequilibrium for this test suggests that the method is well suited to the analysis of dense maps. The relationship between power and family structure is investigated, and these results are applicable to study design in complex disease, especially for late-onset conditions for which parents are usually not available. When parental genotypes are available, power does not depend greatly on the number of offspring in each family. Power decreases when parental genotypes are not available, but the loss in power is negligible when four or more offspring per family are genotyped. Finally, it is shown that, when siblings are available, the total number of genotypes required in order to achieve comparable power is smaller if parents are not genotyped.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10631157 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't10631157zThe Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom. goncalo@well.ox.ac.uk~?>FAbecasis, G. R. Cardon, L. R. Cookson, W. O. Sham, P. C. Cherny, S. S.20017Association analysis in a variance components frameworkS341-6Genet Epidemiol 21 Suppl 1Chromosome Mapping/*statistics & numerical data Chromosomes, Human, Pair 6 Chromosomes, Human, Pair 9 Genetics, Population Humans Lod Score *Models, Genetic Phenotype Polymorphism, Genetic Variation (Genetics)Association analyses conducted in a variance components framework can include information from all available individuals but remain unbiased in the presence of familiality or linkage. Models that include both linkage and association parameters provide different estimates of the effect of a single locus and can be used to distinguish causal polymorphisms from other types of variation. We examine some of these models and their properties in a blind analysis of the simulated Genetic Analysis Workshop 12 data sets.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11793695 g0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11793695SWellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.t~??+Abecasis, G. R. Cherny, S. S. Cardon, L. R.2001NThe impact of genotyping error on family-based analysis of quantitative traits130-4Eur J Hum Genet92Alleles Chromosome Mapping/*methods Computer Simulation Gene Frequency/genetics *Genotype Humans Linkage (Genetics) Lod Score Logistic Models Matched-Pair Analysis Models, Genetic Monte Carlo Method Nuclear Family *Quantitative Trait, Heritable Reproducibility of ResultsFebzErrors in genotyping can substantially influence the power to detect linkage using affected sib-pairs, but it is not clear what effect such errors have on quantitative trait analyses. Here we use Monte Carlo simulation to examine the influence of genotyping error on multipoint vs two-point analysis, variable map density, locus effect size and allele frequency in quantitative trait linkage and association studies of sib-pairs. The analyses are conducted using variance components methods. We contrast the effects of error on quantitative trait analyses with those on the affected sib-pair design. The results indicate that genotyping error influences linkage studies of affected sib pairs more severely than studies of quantitative traits in unselected sibs. In situations of modest effect size, 5% genotyping error eliminates all supporting evidence for linkage to a true susceptibility locus in affected pairs, but may only result in a loss of 15% of linkage information in random pairs. Multipoint analysis does not suffer substantially more than two-point analysis; for moderate error rates (< 5%), multipoint analysis with error is more powerful than two-point with no error. Map density does not appear to be an important factor for linkage analysis. QTL association analyses of common alleles are reasonably robust to genotyping error but power can be affected dramatically with rare alleles.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11313746 g1018-4813 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11313746KWellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.~?@:Abecasis, G. R. Cherny, S. S. Cookson, W. O. Cardon, L. R.20014GRR: graphical representation of relationship errors742-3Bioinformatics178Alleles Computational Biology *Computer Graphics Databases, Genetic Genetics, Medical/*statistics & numerical data Genotype Humans Linkage (Genetics)AugSUMMARY: A graphical tool for verifying assumed relationships between individuals in genetic studies is described. GRR can detect many common errors using genotypes from many markers. AVAILABILITY: GRR is available at http://bioinformatics.well.ox.ac.uk/GRR.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11524377 g1367-4803 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11524377zWellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7RZ, UK. goncalo@well.ox.ac.uk[~?A:Abecasis, G. R. Cherny, S. S. Cookson, W. O. Cardon, L. R.2002IMerlin--rapid analysis of dense genetic maps using sparse gene flow trees97-101 Nat Genet301*Algorithms Female Genotype Haplotypes Humans *Likelihood Functions *Linkage (Genetics) Male Meiosis Pedigree Polymorphism, Genetic *SoftwareJanEfforts to find disease genes using high-density single-nucleotide polymorphism (SNP) maps will produce data sets that exceed the limitations of current computational tools. Here we describe a new, efficient method for the analysis of dense genetic maps in pedigree data that provides extremely fast solutions to common problems such as allele-sharing analyses and haplotyping. We show that sparse binary trees represent patterns of gene flow in general pedigrees in a parsimonious manner, and derive a family of related algorithms for pedigree traversal. With these trees, exact likelihood calculations can be carried out efficiently for single markers or for multiple linked markers. Using an approximate multipoint calculation that ignores the unlikely possibility of a large number of recombinants further improves speed and provides accurate solutions in dense maps with thousands of markers. Our multipoint engine for rapid likelihood inference (Merlin) is a computer program that uses sparse inheritance trees for pedigree analysis; it performs rapid haplotyping, genotype error detection and affected pair linkage analyses and can handle more markers than other pedigree analysis packages.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11731797 y1061-4036 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11731797iThe Wellcome Trust Center for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. goncalo@umich.edu)~?BAbecasis, G. R. Cookson, W. O.20002GOLD--graphical overview of linkage disequilibrium182-3Bioinformatics162NComputer Graphics Haplotypes Humans Linkage Disequilibrium/*genetics *SoftwareFeb0SUMMARY: We describe a software package that provides a graphical summary of linkage disequilibrium in human genetic data. It allows for the analysis of family data and is well suited to the analysis of dense genetic maps. AVAILABILITY: http://www.well.ox.ac.uk/asthma/GOLD CONTACT: goncalo@well.ox.ac.ukfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10842743 B1367-4803 (Print) Journal Article Research Support, Non-U.S. Gov't10842743YWellcome Trust Centre for Human Genetics, University of Oxford, UK. goncalo@well.ox.ac.uk~?C,Abecasis, G. R. Cookson, W. O. Cardon, L. R.2000-Pedigree tests of transmission disequilibrium545-51Eur J Hum Genet87Alleles Chromosome Mapping/*methods Computer Simulation DNA Mutational Analysis Genetic Diseases, Inborn/enzymology/genetics Genotype Humans Linkage Disequilibrium/*genetics Models, Genetic Pedigree Penetrance Peptidyl-Dipeptidase A/*genetics/metabolism Polymorphism, Genetic PrevalenceJulHigh-resolution mapping is essential for the positional cloning of complex disease genes. In outbred populations, linkage disequilibrium is expected to extend for short distances and could provide a powerful fine-mapping tool. Current family-based association tests use nuclear family members to define allelic transmission and controls, but ignore other types of relatives. Here we construct a general approach for scoring allelic transmission that accommodates families of any size and uses all available genotypic information. Family data allows for the construction of an expected genotype for every non-founder, and orthogonal deviates from this expectation are a measure of allelic transmission. These allelic transmission scores can be used to extend previously described tests of linkage disequilibrium for dichotomous or quantitative traits. Some of these tests are illustrated, together with a permutation framework for estimating exact significance levels. Simulation studies are used to investigate power and error rates of the approach. As a practical application, the method is used to investigate the relationship between circulating angiotensin-1 converting enzyme (ACE) levels and polymorphisms in the ACE gene using previously published data.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10909856 B1018-4813 (Print) Journal Article Research Support, Non-U.S. Gov't10909856YWellcome Trust Center for Human Genetics, University of Oxford, UK. goncalo@well.ox.ac.uk ~?D,Abecasis, G. R. Cookson, W. O. Cardon, L. R.2001WThe power to detect linkage disequilibrium with quantitative traits in selected samples1463-74Am J Hum Genet686Alleles Chromosome Mapping/*methods/*statistics & numerical data Computer Simulation Gene Frequency/genetics Humans Linkage Disequilibrium/*genetics Matched-Pair Analysis Models, Genetic Nuclear Family Patient Selection *Quantitative Trait, Heritable Sample Size Sampling StudiesJunResults from power studies for linkage detection have led to many ongoing and planned collections of phenotypically extreme nuclear families. Given the great expense of collecting these families and the imminent availability of a dense diallelic marker map, the families are likely to be used in allelic-association as well as linkage studies. However, optimal selection strategies for linkage may not be equally powerful for association. We examine the power to detect linkage disequilibrium for quantitative traits after phenotypic selection. The results encompass six selection strategies that are in widespread use, including single selection (two designs), affected sib pairs, concordant and discordant pairs, and the extreme-concordant and -discordant design. Selection of sibships on the basis of one extreme proband with high or low trait scores provides as much power as discordant sib pairs but requires the screening and phenotyping of substantially fewer initial families from which to select. Analysis of the role of allele frequencies within each selection design indicates that common trait alleles generally offer the most power, but similarities between the marker- and trait-allele frequencies are much more important than the trait-locus frequency alone. Some of the most widespread selection designs, such as single selection, yield power gains only when both the marker and quantitative trait loci (QTL) are relatively rare in the population. In contrast, discordant pairs and the extreme-proband design provide power for the broadest range of QTL-marker-allele frequency differences. Overall, proband selection from either tail provides the best balance of power, robustness, and simplicity of ascertainment for family-based association analysis.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11349228 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.113492286University of Oxford, Oxford, OX3 7BN, United Kingdom.s~?EAbecasis, G. R. Noguchi, E. Heinzmann, A. Traherne, J. A. Bhattacharyya, S. Leaves, N. I. Anderson, G. G. Zhang, Y. Lench, N. J. Carey, A. Cardon, L. R. Moffatt, M. F. Cookson, W. O.2001JExtent and distribution of linkage disequilibrium in three genomic regions191-197Am J Hum Genet681#Computer Simulation England/ethnology European Continental Ancestry Group/genetics Female Gene Frequency/genetics *Genome, Human Haplotypes/genetics Humans Linkage Disequilibrium/*genetics Male Models, Genetic Pedigree Polymorphism, Genetic/*genetics Polymorphism, Single Nucleotide/geneticsJanThe positional cloning of genes underlying common complex diseases relies on the identification of linkage disequilibrium (LD) between genetic markers and disease. We have examined 127 polymorphisms in three genomic regions in a sample of 575 chromosomes from unrelated individuals of British ancestry. To establish phase, 800 individuals were genotyped in 160 families. The fine structure of LD was found to be highly irregular. Forty-five percent of the variation in disequilibrium measures could be explained by physical distance. Additional factors, such as allele frequency, type of polymorphism, and genomic location, explained <5% of the variation. Nevertheless, disequilibrium was occasionally detectable at 500 kb and was present for over one-half of marker pairs separated by <50 kb. Although these findings are encouraging for the prospects of a genomewide LD map, they suggest caution in interpreting localization due to allelic association.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11083947 k0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.11083947jWellcome Trust Centre for Human Genetics, University of Oxford, Oxford, England OX3 7BN. kayser@eva.mpg.deo~?F Abecasis, G. R. Wigginton, J. E.2005WHandling marker-marker linkage disequilibrium: pedigree analysis with clustered markers754-67Am J Hum Genet775*Algorithms Chromosomes, Human, Pair 17/genetics Computer Simulation Genetic Markers/genetics Humans Linkage Disequilibrium/*genetics Markov Chains Pedigree Polymorphism, Single Nucleotide/*genetics Psoriasis/*geneticsNovsSingle-nucleotide polymorphisms (SNPs) are rapidly replacing microsatellites as the markers of choice for genetic linkage studies and many other studies of human pedigrees. Here, we describe an efficient approach for modeling linkage disequilibrium (LD) between markers during multipoint analysis of human pedigrees. Using a gene-counting algorithm suitable for pedigree data, our approach enables rapid estimation of allele and haplotype frequencies within clusters of tightly linked markers. In addition, with the use of a hidden Markov model, our approach allows for multipoint pedigree analysis with large numbers of SNP markers organized into clusters of markers in LD. Simulation results show that our approach resolves previously described biases in multipoint linkage analysis with SNPs that are in LD. An updated version of the freely available Merlin software package uses the approach described here to perform many common pedigree analyses, including haplotyping and haplotype frequency estimation, parametric and nonparametric multipoint linkage analysis of discrete traits, variance-components and regression-based analysis of quantitative traits, calculation of identity-by-descent or kinship coefficients, and case selection for follow-up association studies. To illustrate the possibilities, we examine a data set that provides evidence of linkage of psoriasis to chromosome 17.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16252236 g0002-9297 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16252236Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA. goncalo@umich.edu. ^~?G*Abreu, P. C. Hodge, S. E. Greenberg, D. A.2002IQuantification of type I error probabilities for heterogeneity LOD scores156-69Genet Epidemiol222lChi-Square Distribution Computer Simulation Genes, Dominant Humans *Lod Score Models, Genetic Nuclear FamilyFeb Locus heterogeneity is a major confounding factor in linkage analysis. When no prior knowledge of linkage exists, and one aims to detect linkage and heterogeneity simultaneously, classical distribution theory of log-likelihood ratios does not hold. Despite some theoretical work on this problem, no generally accepted practical guidelines exist. Nor has anyone rigorously examined the combined effect of testing for linkage and heterogeneity and simultaneously maximizing over two genetic models (dominant, recessive). The effect of linkage phase represents another uninvestigated issue. Using computer simulation, we investigated type I error (P value) of the "admixture" heterogeneity LOD (HLOD) score, i.e., the LOD score maximized over both recombination fraction theta and admixture parameter alpha and we compared this with the P values when one maximizes only with respect to theta (i.e., the standard LOD score). We generated datasets of phase-known and -unknown nuclear families, sizes k = 2, 4, and 6 children, under fully penetrant autosomal dominant inheritance. We analyzed these datasets (1) assuming a single genetic model, and maximizing the HLOD over theta and alpha; and (2) maximizing the HLOD additionally over two dominance models (dominant vs. recessive), then subtracting a 0.3 correction. For both (1) and (2), P values increased with family size k; rose less for phase-unknown families than for phase-known ones, with the former approaching the latter as k increased; and did not exceed the one-sided mixture distribution xi = (1/2) chi1(2) + (1/2) chi2(2). Thus, maximizing the HLOD over theta and alpha appears to add considerably less than an additional degree of freedom to the associated chi1(2) distribution. We conclude with practical guidelines for linkage investigators.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11788961 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11788961kDepartment of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York, USA.4~?HAkey, J. Jin, L. Xiong, M.2001JHaplotypes vs single marker linkage disequilibrium tests: what do we gain?291-300Eur J Hum Genet94Genetic Diseases, Inborn/genetics Genetic Markers Genetic Screening Genetics, Population *Haplotypes Hemochromatosis Humans *Linkage Disequilibrium *Models, Genetic *Models, StatisticalAprThe genetic dissection of complex diseases represents a formidable challenge for modern human genetics. Recently, it has been suggested that linkage disequilibrium (LD) based methods will be a powerful approach for delineating complex disease genes. Most proposed LD test statistics search for association between a single marker and a putative trait locus. However, the power of a single marker association test may suffer because LD information contained in flanking markers is ignored. Intuitively, haplotypes (which can be regarded as a collection of ordered markers) may be more powerful than individual, unorganised markers. In this study, we derive the analytical tools based on standard chi-square statistics to directly investigate and compare the power between multilocus haplotypes and single marker LD tests. More specifically, novel formulas are obtained in order to calculate expected haplotype frequencies of unlimited size. This study demonstrates that the use of haplotypes can significantly improve the power and robustness of mapping disease genes. Additionally, we detail how the power of haplotype based association tests are affected by important population genetic parameters such as the genetic distance between markers and disease locus, mode of disease inheritance, age of trait causing mutation, frequency of associated marker allele, and level of initial LD. Finally, published data from the Hereditary Hemochromatosis disease region is used to illustrate the utility of haplotypes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11313774 !1018-4813 (Print) Journal Article11313774NHuman Genetics Center, University of Texas-Houston, Houston, Texas 77225, USA.O?IBAlberts, B. Johnson, A. Lewis, J. Raff, M. Roberts, K. Walters, P.2002Molecular Biology of the CellNew York and LondonGarland Science4th3?JAllison, D.S. Faith, M.S.2000The concept of genome-wide power and a consideration of its potential use in mapping polygenic traits: the example of sib-pairs.181-188&Advances in Twin and Sib-pair Analysis&T.D. Spector H. Snieder A.J. MacgregorLondonGreenwich Medical Media~?KAllison, D. B.19979Transmission-disequilibrium tests for quantitative traits676-90Am J Hum Genet603Genetic Markers Genetic Techniques *Genetics, Medical Genome Humans *Linkage Disequilibrium Models, Genetic Models, StatisticalMar|The transmission-disequilibrium test (TDT) of Spielman et al. is a family-based linkage-disequilibrium test that offers a powerful way to test for linkage between alleles and phenotypes that is either causal (i.e., the marker locus is the disease/trait allele) or due to linkage disequilibrium. The TDT is equivalent to a randomized experiment and, therefore, is resistant to confounding. When the marker is extremely close to the disease locus or is the disease locus itself, tests such as the TDT can be far more powerful than conventional linkage tests. To date, the TDT and most other family-based association tests have been applied only to dichotomous traits. This paper develops five TDT-type tests for use with quantitative traits. These tests accommodate either unselected sampling or sampling based on selection of phenotypically extreme offspring. Power calculations are provided and show that, when a candidate gene is available (1) these TDT-type tests are at least an order of magnitude more efficient than two common sib-pair tests of linkage; (2) extreme sampling results in substantial increases in power; and (3) if the most extreme 20% of the phenotypic distribution is selectively sampled, across a wide variety of plausible genetic models, quantitative-trait loci explaining as little as 5% of the phenotypic variation can be detected at the .0001 alpha level with <300 observations.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9042929 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9042929{Obesity Research Center, Columbia University College of Physicians and Surgeons, New York, NY 10025, USA. dba8@columbia.edu~?L>Allison, D. B. Heo, M. Schork, N. J. Wong, S. L. Elston, R. C.1998sExtreme selection strategies in gene mapping studies of oligogenic quantitative traits do not always increase power97-107 Hum Hered482]*Chromosome Mapping Humans Linkage (Genetics) *Models, Genetic *Quantitative Trait, HeritableMar-Apr$It is well known that obtaining adequate statistical power to detect linkage to or association with genes for complex quantitative traits can be very difficult. In response, investigators have developed a number of power-enhancing strategies that consider restraints such as genotyping (and/or phenotyping) costs. In the context of both association and sib pair linkage studies of quantitative traits, one of the most widely discussed techniques is the selective sampling of phenotypically extreme individuals. Several papers have demonstrated that such extreme sampling can markedly increase power (under certain circumstances). However, the parenthetical phrase in the previous sentence has generally not been made explicit and it appears to be implied that the more phenotypically extreme the individuals, the more power one has. In this paper, we show by simulation that this is not true under all circumstances. In particular, we show that under oligogenic models, where some biallelic quantitative trait loci (QTLs) have markedly asymmetric allele frequencies and large mean displacement among genotypes, and others have less asymmetric allele frequencies and smaller mean displacement among genotypes, power to detect linkage to or association with the latter QTL can actually decrease by sampling more extreme sib pairs. This suggests that more extreme sampling is not always better. The 'optimal' sampling scheme may depend on both what one suspects the underlying genetic architecture to be and which of the oligogenic QTL one has greatest interest in detecting.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9526169 F0001-5652 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9526169Obesity Research Center, St. Luke's/Roosevelt Hospital, Columbia University College of Physicians and Surgeons, New York, N.Y., USA. dba8@columbia.eduG~?MOAllison, D. B. Thiel, B. St Jean, P. Elston, R. C. Infante, M. C. Schork, N. J.1998\Multiple phenotype modeling in gene-mapping studies of quantitative traits: power advantages1190-201Am J Hum Genet634Chromosome Mapping/*methods Humans *Linkage (Genetics) *Models, Genetic *Models, Statistical *Multifactorial Inheritance Multivariate Analysis Phenotype *Quantitative Trait, Heritable Reproducibility of ResultsOctEGenomewide searches for loci influencing complex human traits and diseases such as diabetes, hypertension, and obesity are often plagued by low power and interpretive difficulties. Attempts to remedy these difficulties have typically relied on, and have promoted the use of, novel subject-ascertainment schemes, larger sample sizes, a greater density of DNA markers, and more-sophisticated statistical modeling and analysis strategies. Many of these remedies can be costly to implement. We investigate the utility of a simple statistical model for the mapping of quantitative-trait loci that incorporates multiple phenotypic or diagnostic endpoints into a gene-mapping analysis. The approach considers finding a linear combination of multiple phenotypic values that maximizes the evidence for linkage to a locus. Our results suggest that substantial increases in the power to map loci can be obtained with the proposed technique, although the increase in power obtained is a function of the size and direction of the residual correlation among the phenotypes used in the analysis. Extensive simulation studies are described that justify these claims, for cases in which two phenotypic measures are analyzed. This approach can be easily extended to cover more-complex situations and may provide a basis for more insightful genetic-analysis paradigms.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9758596 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9758596~Obesity Research Center, St. Luke's/Roosevelt Hospital, Columbia University College of Physicians and Surgeons, New York, USA. ~~?NOAllison, D. B. Neale, M. C. Zannolli, R. Schork, N. J. Amos, C. I. Blangero, J.1999uTesting the robustness of the likelihood-ratio test in a variance-component quantitative-trait loci-mapping procedure531-44Am J Hum Genet652Analysis of Variance *Chromosome Mapping Computer Simulation Humans *Likelihood Functions *Linkage (Genetics) Matched-Pair Analysis Nuclear Family Phenotype *Quantitative Trait, Heritable Reproducibility of Results Sample Size Software Statistical DistributionsAugDetection of linkage to genes for quantitative traits remains a challenging task. Recently, variance components (VC) techniques have emerged as among the more powerful of available methods. As often implemented, such techniques require assumptions about the phenotypic distribution. Usually, multivariate normality is assumed. However, several factors may lead to markedly nonnormal phenotypic data, including (a) the presence of a major gene (not necessarily linked to the markers under study), (b) some types of gene x environment interaction, (c) use of a dichotomous phenotype (i.e., affected vs. unaffected), (d) nonnormality of the population within-genotype (residual) distribution, and (e) selective (extreme) sampling. Using simulation, we have investigated, for sib-pair studies, the robustness of the likelihood-ratio test for a VC quantitative-trait locus-detection procedure to violations of normality that are due to these factors. Results showed (a) that some types of nonnormality, such as leptokurtosis, produced type I error rates in excess of the nominal, or alpha, levels whereas others did not; and (b) that the degree of type I error-rate inflation appears to be directly related to the residual sibling correlation. Potential solutions to this problem are discussed. Investigators contemplating use of this VC procedure are encouraged to provide evidence that their trait data are normally distributed, to employ a procedure that allows for nonnormal data, or to consider implementation of permutation tests.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10417295 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.10417295Obesity Research Center, St. Luke's/Roosevelt Hospital, Columbia University College of Physicians & Surgeons, New York, NY, USA. dba8@columbia.edu~?O6Allison, D. B. Fernandez, J. R. Heo, M. Beasley, T. M.2000ZTesting the robustness of the new Haseman-Elston quantitative-trait loci-mapping procedure249-52Am J Hum Genet6712Chromosome Mapping/*methods/*statistics & numerical data Computer Simulation Humans Least-Squares Analysis Linkage (Genetics)/genetics Matched-Pair Analysis Models, Genetic Nuclear Family Phenotype *Quantitative Trait, Heritable Reproducibility of Results Research Design Software Statistical DistributionsJulVariance components (VC) techniques have emerged as among the more powerful methods for detection of quantitative-trait loci (QTL) in linkage analysis. Allison et al. found that, with particularly marked leptokurtosis in the phenotypic distribution and moderate-to-high residual sibling correlation, maximum likelihood (ML) VC methods may produce a severe excess of type I errors. The new Haseman-Elston (NHE) method is a least-squares-based VC method for mapping of QTL in sib pairs (Elston et al.). Using simulation, we investigate the robustness of the NHE to marked nonnormality, by means of the same distributions and worst-case conditions identified by Allison et al. for the ML approach (i.e., 100 pairs; high residual sibling correlation). Results showed that, when marked nonnormality is present, the NHE can be used without severe type I error-rate inflation, even at very small alpha levels.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10820126 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10820126Obesity Research Center, St. Luke's/Roosevelt Hospital, Columbia University College of Physicians & Surgeons, New York, NY 10025, USA. dba8@columbia.edu~?P#Almasy, L. Dyer, T. D. Blangero, J.1997UBivariate quantitative trait linkage analysis: pleiotropy versus co-incident linkages953-8Genet Epidemiol146Chromosome Mapping/methods *Chromosomes, Human, Pair 8 Humans Likelihood Functions *Linkage (Genetics) Lod Score Pedigree Phenotype *Quantitative Trait, HeritableSPower to detect linkage and localization of a major gene were compared in univariate and bivariate variance components linkage analysis of three related quantitative traits in general pedigrees. Although both methods demonstrated adequate power to detect loci of moderate effect, bivariate analysis improved both power and localization for correlated quantitative traits mapping to the same chromosomal region, regardless of whether co-localization was the result of pleiotropy. Additionally, a test of pleiotropy versus co-incident linkage was shown to have adequate power and a low error rate.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9433606 X0741-0395 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.9433606iDepartment of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas 78245-0549, USA.~?QAlmasy, L. Blangero, J.1998CMultipoint quantitative-trait linkage analysis in general pedigrees1198-211Am J Hum Genet625h*Computer Simulation Humans *Linkage (Genetics) *Models, Genetic Pedigree *Quantitative Trait, HeritableMayMultipoint linkage analysis of quantitative-trait loci (QTLs) has previously been restricted to sibships and small pedigrees. In this article, we show how variance-component linkage methods can be used in pedigrees of arbitrary size and complexity, and we develop a general framework for multipoint identity-by-descent (IBD) probability calculations. We extend the sib-pair multipoint mapping approach of Fulker et al. to general relative pairs. This multipoint IBD method uses the proportion of alleles shared identical by descent at genotyped loci to estimate IBD sharing at arbitrary points along a chromosome for each relative pair. We have derived correlations in IBD sharing as a function of chromosomal distance for relative pairs in general pedigrees and provide a simple framework whereby these correlations can be easily obtained for any relative pair related by a single line of descent or by multiple independent lines of descent. Once calculated, the multipoint relative-pair IBDs can be utilized in variance-component linkage analysis, which considers the likelihood of the entire pedigree jointly. Examples are given that use simulated data, demonstrating both the accuracy of QTL localization and the increase in power provided by multipoint analysis with 5-, 10-, and 20-cM marker maps. The general pedigree variance component and IBD estimation methods have been implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) computer package.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9545414 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9545414}Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX 78245-0549, USA. almasy@darwin.sfbr.org`~?R.Almasy, L. Towne, B. Peterson, C. Blangero, J.2001$Detecting genotype x age interactionS819-24Genet Epidemiol 21 Suppl 1'Adult Age Factors Aged Alleles Analysis of Variance Chromosome Mapping/statistics & numerical data Chromosomes, Human, Pair 17 Chromosomes, Human, Pair 9 Female Genetic Predisposition to Disease/*genetics *Genotype Humans Lod Score Male Middle Aged *Models, Genetic Quantitative Trait, HeritableWThis paper explores several extensions to the variance component method, which incorporate genotype x age interaction effects. We evaluate the performance of these methods for detecting genotype x age interaction in quantitative genetic analyses of a quantitative trait (Q4), contrasting this with false positive detection rates obtained from a phenotype influenced by the same genes but without genotype x age interaction effects (Q3). We then assess the impact on linkage power and false positive rate of allowing a QTL-specific genotype x age interaction in linkage analysis of these same traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11793786 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11793786Department of Genetics, Southwest Foundation for Biomedical Research, 7620 NW Loop 410, P.O. Box 760549, San Antonio, TX 78245-0549, USA. ~?SAltshuler, D. Hirschhorn, J. N. Klannemark, M. Lindgren, C. M. Vohl, M. C. Nemesh, J. Lane, C. R. Schaffner, S. F. Bolk, S. Brewer, C. Tuomi, T. Gaudet, D. Hudson, T. J. Daly, M. Groop, L. Lander, E. S.2000_The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes76-80 Nat Genet261Adult Age of Onset Aged Alanine/genetics Alleles Blood Glucose/genetics Blood Pressure/genetics Body Mass Index Cholesterol/genetics Diabetes Mellitus, Type 2/*genetics Family Health Fathers Female Genotype Humans Linkage Disequilibrium Lipoproteins, HDL/genetics Male Middle Aged Models, Genetic Mothers Phenotype *Polymorphism, Genetic Proline/genetics Receptors, Cytoplasmic and Nuclear/*genetics Risk Factors Transcription Factors/*geneticsSep?Genetic association studies are viewed as problematic and plagued by irreproducibility. Many associations have been reported for type 2 diabetes, but none have been confirmed in multiple samples and with comprehensive controls. We evaluated 16 published genetic associations to type 2 diabetes and related sub-phenotypes using a family-based design to control for population stratification, and replication samples to increase power. We were able to confirm only one association, that of the common Pro12Ala polymorphism in peroxisome proliferator-activated receptor-gamma(PPARgamma) with type 2 diabetes. By analysing over 3,000 individuals, we found a modest (1.25-fold) but significant (P=0.002) increase in diabetes risk associated with the more common proline allele (85% frequency). Moreover, our results resolve a controversy about common variation in PPARgamma. An initial study found a threefold effect, but four of five subsequent publications failed to confirm the association. All six studies are consistent with the odds ratio we describe. The data implicate inherited variation in PPARgamma in the pathogenesis of type 2 diabetes. Because the risk allele occurs at such high frequency, its modest effect translates into a large population attributable risk-influencing as much as 25% of type 2 diabetes in the general population.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10973253 P1061-4036 (Print) Journal Article Meta-Analysis Research Support, Non-U.S. Gov't10973253RWhitehead Institute/MIT Center for Genome Research, Cambridge, Massachusetts, USA.Q~?TPAltshuler, D. Brooks, L.D. Chakravarti, A. Collins, F.S. Daly, M.J. Donnelly, P.2005#A haplotype map of the human genome1299-320Nature4377063Chromosomes, Human, Y/genetics DNA, Mitochondrial/genetics Gene Frequency/genetics *Genome, Human Haplotypes/*genetics Humans Linkage Disequilibrium/genetics Polymorphism, Single Nucleotide/*genetics Recombination, Genetic/genetics Selection (Genetics)Oct 27Inherited genetic variation has a critical but as yet largely uncharacterized role in human disease. Here we report a public database of common variation in the human genome: more than one million single nucleotide polymorphisms (SNPs) for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted. These data document the generality of recombination hotspots, a block-like structure of linkage disequilibrium and low haplotype diversity, leading to substantial correlations of SNPs with many of their neighbours. We show how the HapMap resource can guide the design and analysis of genetic association studies, shed light on structural variation and recombination, and identify loci that may have been subject to natural selection during human evolution.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16255080 1476-4687 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.16255080?U!American Psychiatric Association,1995VDiagnostic and Statistical Manual of Mental Disorders, 4th Edn: Primary Care Version. Arlington, VA.American Psychiatric Publishing~?V Amos, C. I.1994NRobust variance-components approach for assessing genetic linkage in pedigrees535-43Am J Hum Genet543*Family Genetic Markers Humans *Linkage (Genetics) Mathematics *Models, Genetic Nuclear Family *Pedigree Probability *Variation (Genetics)MarTo assess evidence for genetic linkage from pedigrees, I developed a limited variance-components approach. In this method, variability among trait observations from individuals within pedigrees is expressed in terms of fixed effects from covariates and effects due to an unobservable trait-affecting major locus, random polygenic effects, and residual nongenetic variance. The effect attributable to a locus linked to a marker is a function of the additive and dominance components of variance of the locus, the recombination fraction, and the proportion of genes identical by descent at the marker locus for each pair of sibs. For unlinked loci, the polygenic variance component depends only on the relationship between the relative pair. Parameters can be estimated by either maximum-likelihood methods or quasi-likelihood methods. The forms of quasi-likelihood estimators are provided. Hypothesis tests derived from the maximum-likelihood approach are constructed by appeal to asymptotic theory. A simulation study showed that the size of likelihood-ratio tests was appropriate but that the monogenic component of variance was generally underestimated by the likelihood approach.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8116623 !0002-9297 (Print) Journal Article8116623\Department of Epidemiology, University of Texas, M.D. Anderson Cancer Center, Houston 77030.)~?WDAmos, C. I. Elston, R. C. Bonney, G. E. Keats, B. J. Berenson, G. S.1990yA multivariate method for detecting genetic linkage, with application to a pedigree with an adverse lipoprotein phenotype247-54Am J Hum Genet472Apolipoproteins/blood/genetics Cholesterol, HDL/blood/genetics Cholesterol, LDL/blood/genetics Coronary Disease/blood/*genetics Genetic Markers Humans *Linkage (Genetics) Lipoproteins/blood/*genetics *Models, Genetic Multivariate Analysis Pedigree Phenotype Risk FactorsAug8The robust or model-free method for detecting linkage developed by Haseman and Elston for data from sib pairs is extended to incorporate observations of multiple traits on each individual. A method is proposed that estimates the linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. The method is illustrated by the study of apolipoprotein and cholesterol levels in individuals from a large family that had many members diagnosed with coronary heart disease.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2378349 !0002-9297 (Print) Journal Article2378349FFamily Studies Section, National Cancer Institute, Bethesda, MD 20892.~?XAnttila, V. Kallela, M. Oswell, G. Kaunisto, M. A. Nyholt, D. R. Hamalainen, E. Havanka, H. Ilmavirta, M. Terwilliger, J. Sobel, E. Peltonen, L. Kaprio, J. Farkkila, M. Wessman, M. Palotie, A.2006PTrait components provide tools to dissect the genetic susceptibility of migraine85-99Am J Hum Genet791Chromosome Mapping Female Genetic Heterogeneity *Genetic Predisposition to Disease Humans Lod Score Male Migraine Disorders/*geneticsJul3The commonly used "end diagnosis" phenotype that is adopted in linkage and association studies of complex traits is likely to represent an oversimplified model of the genetic background of a disease. This is also likely to be the case for common types of migraine, for which no convincingly associated genetic variants have been reported. In headache disorders, most genetic studies have used end diagnoses of the International Headache Society (IHS) classification as phenotypes. Here, we introduce an alternative strategy; we use trait components--individual clinical symptoms of migraine--to determine affection status in genomewide linkage analyses of migraine-affected families. We identified linkage between several traits and markers on chromosome 4q24 (highest LOD score under locus heterogeneity [HLOD] 4.52), a locus we previously reported to be linked to the end diagnosis migraine with aura. The pulsation trait identified a novel locus on 17p13 (HLOD 4.65). Additionally, a trait combination phenotype (IHS full criteria) revealed a locus on 18q12 (HLOD 3.29), and the age at onset trait revealed a locus on 4q28 (HLOD 2.99). Furthermore, suggestive or nearly suggestive evidence of linkage to four additional loci was observed with the traits phonophobia (10q22) and aggravation by physical exercise (12q21, 15q14, and Xp21), and, interestingly, these loci have been linked to migraine in previous studies. Our findings suggest that the use of symptom components of migraine instead of the end diagnosis provides a useful tool in stratifying the sample for genetic studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16773568 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't16773568)Finnish Genome Center, Helsinki, Finland.u~?YGAo, S. I. Yip, K. Ng, M. Cheung, D. Fong, P. Y. Melhado, I. Sham, P. C.2005ICLUSTAG: hierarchical clustering and graph methods for selecting tag SNPs1735-6Bioinformatics218*Algorithms Chromosome Mapping/*methods Cluster Analysis DNA Mutational Analysis/*methods *Expressed Sequence Tags Genetics, Population Genome, Human Humans Pattern Recognition, Automated/*methods Polymorphism, Single Nucleotide/*genetics *SoftwareApr 15SUMMARY: Cluster and set-cover algorithms are developed to obtain a set of tag single nucleotide polymorphisms (SNPs) that can represent all the known SNPs in a chromosomal region, subject to the constraint that all SNPs must have a squared correlation R2>C with at least one tag SNP, where C is specified by the user. AVAILABILITY: http://hkumath.hku.hk/web/link/CLUSTAG/CLUSTAG.html CONTACT: mng@maths.hku.hk.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15585525 B1367-4803 (Print) Journal Article Research Support, Non-U.S. Gov't15585525LDepartment of Mathematics, The University of Hong Kong, Pokfulam, Hong Kong.B~?Z(Ardlie, K. G. Kruglyak, L. Seielstad, M.20026Patterns of linkage disequilibrium in the human genome299-309 Nat Rev Genet34Animals Chromosome Mapping Demography Drosophila/genetics Genetics, Population *Genome, Human Haplotypes Humans *Linkage Disequilibrium Pattern Recognition, Automated Polymorphism, Single NucleotideAprParticular alleles at neighbouring loci tend to be co-inherited. For tightly linked loci, this might lead to associations between alleles in the population a property known as linkage disequilibrium (LD). LD has recently become the focus of intense study in the hope that it might facilitate the mapping of complex disease loci through whole-genome association studies. This approach depends crucially on the patterns of LD in the human genome. In this review, we draw on empirical studies in humans and Drosophila, as well as simulation studies, to assess the current state of knowledge about patterns of LD, and consider the implications for the use of LD as a mapping tool.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11967554 n1471-0056 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Review11967554LGenomics Collaborative, 99 Erie Street, Cambridge, Massachusetts 02139, USA.u~?[5Blair, E. L. Wakefield, M. Ingram, G. I. Armitage, P.1955NLowered capillary resistance after iontophoresis of lysergic acid diethylamide563Nature1764481*Capillaries *Ion ExchangeSep 17fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=13265778 !0028-0836 (Print) Journal Article13265778w?\ Armitage, P19557Tests for linear trends in proportions and frequencies.375-386 Biometrics 11~?] Atwood, L. D. Heard-Costa, N. L.2003+Limits of fine-mapping a quantitative trait99-106Genet Epidemiol242Chromosome Mapping/*methods Computer Simulation Humans Lod Score Nuclear Family *Quantitative Trait, Heritable Statistics, NonparametricFebOnce a significant linkage is found, an important goal is reducing the error in the estimated location of the linked locus. A common approach to reducing location error, called fine-mapping, is the genotyping of additional markers in the linked region to increase the genetic information. The utility of fine-mapping for quantitative trait linkage analysis is largely unknown. To explore this issue, we performed a fine-mapping simulation in which the region containing a significant linkage at a 10-centiMorgan (cM) resolution was fine-mapped at 2, 1, and 0.5 cM. We simulated six quantitative trait models in which the proportion of variation due to the quantitative trait locus (QTL) ranged from 0.20-0.90. We used four sampling designs that were all combinations of 100 and 200 families of sizes 5 and 7. Variance components linkage analysis (Genehunter) was performed until 1,000 replicates were found with a maximum lodscore greater than 3.0. For each of these 1,000 replications, we repeated the linkage analysis three times: once for each of the fine-map resolutions. For the most realistic model, reduction in the average location error ranged from 3-15% for 2-cM fine-mapping and from 3-18% for 1-cM fine-mapping, depending on the number of families and family size. Fine-mapping at 0.5 cM did not differ from the 1-cM results. Thus, if the QTL accounts for a small proportion of the variation, as is the case for realistic traits, fine-mapping has little value.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12548671 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.12548671yDepartment of Neurology, Boston University School of Medicine, 715 Albany Street B-609, Boston, MA 02118, USA. lda@bu.edun?^ Azzelini, A1996-Statistical Inference Based on the LikelihoodLondonChapman and Hall~?_ Bacanu, S. A.2005GRobust estimation of critical values for genome scans to detect linkage24-32Genet Epidemiol281Algorithms Chromosome Mapping/*methods/statistics & numerical data Computer Simulation Eating Disorders/genetics Family Health Female *Genome, Human Humans Linkage (Genetics)/*genetics Male Microsatellite Repeats Models, GeneticJan Estimation of study specific critical values for linkage scans (suggestive and significant thresholds) is important to identify promising regions. In this report, I propose a fast and concrete recipe for finding study specific critical values. Previously, critical values were derived theoretically or empirically. Theoretically-derived values are often conservative due to their assumption of fully informative transmissions. Empirically-derived critical values are computer and skill intensive and may not even be computationally feasible for large pedigrees. In this report, I propose a method to estimate critical values for multipoint linkage analysis using standard, widely used statistical software. The proposed method estimates study-specific critical values by using Autoregressive (AR) models to estimate the correlation between standard normal statistics at adjacent map points and then use this correlation to estimate study-specific critical values. The AR-based method is evaluated using different family structures and density of markers, under both the null hypothesis of no linkage and the alternative hypothesis of linkage between marker and disease locus. Simulations results show the AR-based method accurately predicts critical values for a wide range of study designs.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15372617 k0741-0395 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S.15372617|Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213, USA. bacanus@upmc.edu 9~?`#Bacanu, S. A. Devlin, B. Roeder, K.2000The power of genomic control1933-44Am J Hum Genet666eAlleles Case-Control Studies Chromosome Mapping/*methods/*statistics & numerical data Computer Simulation Europe Female Gene Frequency/genetics *Genetics, Population *Genome, Human Genotype Humans Linkage Disequilibrium/genetics Male Models, Genetic Nuclear Family Polymorphism, Genetic/genetics Probability Regression Analysis Variation (Genetics)/geneticsJunAlthough association analysis is a useful tool for uncovering the genetic underpinnings of complex traits, its utility is diminished by population substructure, which can produce spurious association between phenotype and genotype within population-based samples. Because family-based designs are robust against substructure, they have risen to the fore of association analysis. Yet, if population substructure could be ignored, this robustness can come at the price of power. Unfortunately it is rarely evident when population substructure can be ignored. Devlin and Roeder recently have proposed a method, termed "genomic control" (GC), which has the robustness of family-based designs even though it uses population-based data. GC uses the genome itself to determine appropriate corrections for population-based association tests. Using the GC method, we contrast the power of two study designs, family trios (i.e., father, mother, and affected progeny) versus case-control. For analysis of trios, we use the TDT test. When population substructure is absent, we find GC is always more powerful than TDT; furthermore, contrary to previous results, we show that as a disease becomes more prevalent the discrepancy in power becomes more extreme. When population substructure is present, however, the results are more complex: TDT is more powerful when population substructure is substantial, and GC is more powerful otherwise. We also explore general issues of power and implementation of GC within the case-control setting and find that, economically, GC is at least comparable to and often less expensive than family-based methods. Therefore, GC methods should prove a useful complement to family-based methods for the genetic analysis of complex traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10801388 0002-9297 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.10801388vDepartment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA. bacanus@msx.upmc.edu~?a#Bacanu, S. A. Devlin, B. Roeder, K.2002EAssociation studies for quantitative traits in structured populations78-93Genet Epidemiol221Alleles Genetic Markers Genetic Predisposition to Disease/*epidemiology Genome, Human Genotype Humans *Models, Genetic Phenotype *Polymorphism, Genetic Probability *Quantitative Trait, HeritableJanAssociation between disease and genetic polymorphisms often contributes critical information in our search for the genetic components of common diseases. Devlin and Roeder [1999: Biometrics 55:997-1004] introduced genomic control, a statistical method that overcomes a drawback to the use of population-based samples for tests of association, namely spurious associations induced by population structure. In essence, genomic control (GC) uses markers throughout the genome to adjust for any inflation in test statistics due to substructure. To date, genomic control (GC) has been developed for binary traits and bi- or multiallelic markers. Tests of association using GC have been limited to single genes. In this report, we generalize GC to quantitative traits (QT) and multilocus models. Using statistical analysis and simulations, we show that GC controls spurious associations in reasonable settings of population substructure for QT models, including gene-gene interaction. Through simulations, we explore GC power for both random and selected samples, assuming the QT locus tested is causal and its specific heritability is 2.5-5%. We find that GC, combined with either random or selected samples, has good power in this setting, and that more complex models induce smaller GC corrections. The latter suggests greater power can be achieved by specifying more complex genetic models, but this observation only follows when such models are largely correct and specified a priori.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11754475 o0741-0395 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.11754475Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O'Hara Street, Pittsburgh, PA 15213, USA. roeder@stat.cmu.edu~?cKBian, L. Yang, J. D. Guo, T. W. Duan, Y. Qin, W. Sun, Y. Feng, G. Y. He, L.2005UAssociation study of the A2M and LRP1 Genes with Alzheimer disease in the Han Chinese731-7Biol Psychiatry589HAged Alleles Alzheimer Disease/*genetics Apolipoproteins E/genetics China/epidemiology DNA/genetics Data Interpretation, Statistical Female Gene Frequency Genetic Markers Genotype Haplotypes Humans LDL-Receptor Related Protein 1/*genetics Logistic Models Male Polymorphism, Genetic Risk Assessment alpha-Macroglobulins/*geneticsNov 1BACKGROUND: Low-density lipoprotein receptor-related protein 1 (LRP1) and alpha-2-macroglobulin (A2M) are two plausible candidate genes for Alzheimer disease (AD) based on their important biological function and positional information. To date, numerous studies have investigated their possible association with AD but the results are controversial. METHODS: To investigate the potential genetic contribution of the two genes in the Han Chinese population, we performed a case-control association study using 10 polymorphisms (4 in LRP1 and 6 in A2M) that span approximately the whole corresponding gene. RESULTS: Comparison of allele, genotype, and haplotype frequencies for polymorphisms in A2M revealed no significant differences between patients and control subjects. For the LRP1 gene, however, we found an overrepresentation of the CTCG haplotype in the control group (p = .002). The difference was still of statistical significance in the apolipoprotein E (APOE) epsilon 4 negative subjects (p(CTCG) = .003). Multiple logistic regression analysis did not show any evidence of synergism between A2M, LRP1, and APOE. CONCLUSIONS: Our results indicate that the CTCG haplotype of LRP1 may reduce the risk of late-onset AD, but A2M is not associated with this disease in the Han Chinese population.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16040006 B0006-3223 (Print) Journal Article Research Support, Non-U.S. Gov't16040006Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Bio-X Center, Shanghai Jiao Tong University, Shanghai, China.~?dBalding, D. J.2006DA tutorial on statistical methods for population association studies781-91 Nat Rev Genet710Genetics, Medical/*methods Genetics, Population/*methods Genome, Human/genetics Genotype Haplotypes Humans Polymorphism, Single Nucleotide/genetics Statistics/*methodsOct`Although genetic association studies have been with us for many years, even for the simplest analyses there is little consensus on the most appropriate statistical procedures. Here I give an overview of statistical approaches to population association studies, including preliminary analyses (Hardy-Weinberg equilibrium testing, inference of phase and missing data, and SNP tagging), and single-SNP and multipoint tests for association. My goal is to outline the key methods with a brief discussion of problems (population structure and multiple testing), avenues for solutions and some ongoing developments.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16983374 I1471-0056 (Print) Journal Article Research Support, Non-U.S. Gov't Review16983374Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London W2 1PG, UK. d.balding@imperial.ac.uk ~?eSBarnholtz, J. S. de Andrade, M. Page, G. P. King, T. M. Peterson, L. E. Amos, C. I.1999nAssessing linkage of monoamine oxidase B in a genome-wide scan using a univariate variance components approachS49-54Genet Epidemiol 17 Suppl 1Chromosomes, Human, Pair 1 Chromosomes, Human, Pair 12 Chromosomes, Human, Pair 4 Chromosomes, Human, Pair 9 Family Health Genetic Markers *Genetic Screening *Genome Humans *Linkage (Genetics) Lod Score Monoamine Oxidase/*genetics *Quantitative Trait, HeritableWe report results when one alcoholism related quantitative trait, monoamine oxidase B (MAOB), is analyzed by the variance components approach for linkage [Amos, 1994; Amos et al., 1996] using the Collaborative Study on the Genetics of Alcoholism data set provided for the Genetic Analysis Workshop 11. We used two different covariate models, one with age at interview, sex, ethnicity, and smoking status and the other with age at interview, sex, and ethnicity. The univariate analysis showed 24 markers on four different chromosomes (1, 4, 9, and 12) to have evidence for linkage with the quantitative trait (single-point and multipoint linkage). However, when outliers for MAOB were removed, the significant evidence for linkage disappeared.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10597411 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10597411XDepartment of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, USA.#~?fBarrett, J. C. Cardon, L. R.20066Evaluating coverage of genome-wide association studies659-62 Nat Genet386Case-Control Studies *Genome, Human Haplotypes Humans Likelihood Functions Linkage Disequilibrium *Polymorphism, Single NucleotideJunGenome-wide association studies involving hundreds of thousands of SNPs in thousands of cases and controls are now underway. The first of many analytical challenges in these studies involves the choice of SNPs to genotype. It is not practical to construct a different panel of tag SNPs for each study, so the first generation of genome-wide scans will use predefined, commercially available marker panels, which will in part dictate their success or failure. We compare different approaches in use today, and show that although many of them provide substantial coverage of common variation in non-African populations, the precise extent is strongly dependent on the frequencies of alleles of interest and on specific considerations of study design. Overall, despite substantial differences in genotyping technologies, marker selection strategies and number of markers assayed, the first-generation high-throughput platforms all offer similar levels of genome coverage.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16715099 g1061-4036 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16715099dWellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. >~?gKBarrett, S. Beck, J. C. Bernier, R. Bisson, E. Braun, T. A. Casavant, T. L. Childress, D. Folstein, S. E. Garcia, M. Gardiner, M. B. Gilman, S. Haines, J. L. Hopkins, K. Landa, R. Meyer, N. H. Mullane, J. A. Nishimura, D. Y. Palmer, P. Piven, J. Purdy, J. Santangelo, S. L. Searby, C. Sheffield, V. Singleton, J. Slager, S. et al.,1999MAn autosomal genomic screen for autism. Collaborative linkage study of autism609-15Am J Med Genet886rAdolescent Adult Autistic Disorder/etiology/*genetics Child Child, Preschool *Chromosome Mapping Chromosomes, Human, Pair 13/genetics Chromosomes, Human, Pair 7/genetics Family Health Female Gene Frequency Genes, Recessive/genetics Genetic Predisposition to Disease/*genetics *Genetic Screening Humans Intelligence Tests Linkage (Genetics)/*genetics Male Models, GeneticDec 15Autism is a severe neurodevelopmental disorder defined by social and communication deficits and ritualistic-repetitive behaviors that are detectable in early childhood. The etiology of idiopathic autism is strongly genetic, and oligogenic transmission is likely. The first stage of a two-stage genomic screen for autism was carried out by the Collaborative Linkage Study of Autism on individuals affected with autism from 75 families ascertained through an affected sib-pair. The strongest multipoint results were for regions on chromosomes 13 and 7. The highest maximum multipoint heterogeneity LOD (MMLS/het) score is 3.0 at D13S800 (approximately 55 cM from the telomere) under the recessive model, with an estimated 35% of families linked to this locus. The next highest peak is an MMLS/het score of 2.3 at 19 cM, between D13S217 and D13S1229. Our third highest MMLS/het score of 2.2 is on chromosome 7 and is consistent with the International Molecular Genetic Study of Autism Consortium report of a possible susceptibility locus somewhere within 7q31-33. These regions and others will be followed up in the second stage of our study by typing additional markers in both the original and a second set of identically ascertained autism families, which are currently being collected. By comparing results across a number of studies, we expect to be able to narrow our search for autism susceptibility genes to a small number of genomic regions. Am. J. Med. Genet. (Neuropsychiatr. Genet.) 88:609-615, 1999.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10581478 F0148-7299 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10581478EThe Johns Hopkins University School of Medicine, Baltimore, Maryland.~?h-Barrett, J. C. Fry, B. Maller, J. Daly, M. J.2005>Haploview: analysis and visualization of LD and haplotype maps263-5Bioinformatics212*Algorithms Chromosome Mapping/*methods Haplotypes/*genetics Internet Linkage Disequilibrium/*genetics Programming Languages Sequence Alignment/*methods Sequence Analysis, DNA/*methods *Software *User-Computer InterfaceJan 15%SUMMARY: Research over the last few years has revealed significant haplotype structure in the human genome. The characterization of these patterns, particularly in the context of medical genetic association studies, is becoming a routine research activity. Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface. AVAILABILITY: http://www.broad.mit.edu/mpg/haploview/ CONTACT: jcbarret@broad.mit.edufhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15297300 !1367-4803 (Print) Journal Article15297300\Whitehead Institute for Biomedical Research Cambridge, MA 02142, USA. jcbarret@broad.mit.edur~?i*Bartlett, C. W. Goedken, R. Vieland, V. J.2005xEffects of updating linkage evidence across subsets of data: reanalysis of the autism genetic resource exchange data set688-95Am J Hum Genet764Autistic Disorder/*genetics Chromosomes, Human, Pair 1 Chromosomes, Human, Pair 17 *Data Interpretation, Statistical Genetic Heterogeneity Humans *Linkage (Genetics) *Models, Genetic Models, Statistical Nuclear FamilyApr%Results of autism linkage studies have been difficult to interpret across research groups, prompting the use of ever-increasing sample sizes to increase power. However, increasing sample size by pooling disparate collections for a single analysis may, in fact, not increase power in the face of genetic heterogeneity. Here, we applied the posterior probability of linkage (PPL), a method designed specifically to analyze multiple heterogeneous data sets, to the Autism Genetic Resource Exchange collection of families by analyzing six clinically defined subsets of the data and updating the PPL sequentially over the subsets. Our results indicate a substantial probability of linkage to chromosome 1, which had been previously overlooked; our findings also provide a further characterization of the possible parent-of-origin effects at the 17q11 locus that were previously described in this sample. This analysis illustrates that the way in which heterogeneity is addressed in linkage analysis can dramatically affect the overall conclusions of a linkage study.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15729670 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.15729670Center for Statistical Genetics Research, College of Public Health, and Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA, USA. christopher-bartlett@uiowa.edun?j Bateson, W1909!Mendel’s Principles of Heredity CambridgeCambridge University Press ~?kBeekman, M. Heijmans, B. T. Martin, N. G. Whitfield, J. B. Pedersen, N. L. DeFaire, U. Snieder, H. Lakenberg, N. Suchiman, H. E. de Knijff, P. Frants, R. R. van Ommen, G. J. Kluft, C. Vogler, G. P. Boomsma, D. I. Slagboom, P. E.2003`Evidence for a QTL on chromosome 19 influencing LDL cholesterol levels in the general population845-50Eur J Hum Genet1111LAdolescent Adult Aged Cardiovascular Diseases/genetics Cholesterol, LDL/*genetics *Chromosomes, Human, Pair 19 European Continental Ancestry Group Female Gene Frequency Genetic Markers Genetics, Population Humans *Linkage (Genetics) Lod Score Male Middle Aged Phenotype *Quantitative Trait Loci Twins, Dizygotic Variation (Genetics)NovThe genetic basis of cardiovascular disease (CVD) with its complex etiology is still largely elusive. Plasma levels of lipids and apolipoproteins are among the major quantitative risk factors for CVD and are well-established intermediate traits that may be more accessible to genetic dissection than clinical CVD end points. Chromosome 19 harbors multiple genes that have been suggested to play a role in lipid metabolism and previous studies indicated the presence of a quantitative trait locus (QTL) for cholesterol levels in genetic isolates. To establish the relevance of genetic variation at chromosome 19 for plasma levels of lipids and apolipoproteins in the general, out-bred Caucasian population, we performed a linkage study in four independent samples, including adolescent Dutch twins and adult Dutch, Swedish and Australian twins totaling 493 dizygotic twin pairs. The average spacing of short-tandem-repeat markers was 6-8 cM. In the three adult twin samples, we found consistent evidence for linkage of chromosome 19 with LDL cholesterol levels (maximum LOD scores of 4.5, 1.7 and 2.1 in the Dutch, Swedish and Australian sample, respectively); no indication for linkage was observed in the adolescent Dutch twin sample. The QTL effects in the three adult samples were not significantly different and a simultaneous analysis of the samples increased the maximum LOD score to 5.7 at 60 cM pter. Bivariate analyses indicated that the putative LDL-C QTL also contributed to the variance in ApoB levels, consistent with the high genetic correlation between these phenotypes. Our study provides strong evidence for the presence of a QTL on chromosome 19 with a major effect on LDL-C plasma levels in outbred Caucasian populations.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14571269 g1018-4813 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.14571269oSection of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands. M.Beekman@lumc.nl?lBenjamini, Y. Hochberg, Y1995\Controlling the false discovery rate: a practical and powerful approach to multiple testing.289-300J. R. Stat. Soc. B57t?mBentler, P.M. 1995'EQS Structural Equations Program Manual Encino, CAMultivariate Software ~?nBersaglieri, T. Sabeti, P. C. Patterson, N. Vanderploeg, T. Schaffner, S. F. Drake, J. A. Rhodes, M. Reich, D. E. Hirschhorn, J. N.2004JGenetic signatures of strong recent positive selection at the lactase gene1111-20Am J Hum Genet746European Continental Ancestry Group/*genetics Gene Frequency *Genetics, Population Haplotypes/*genetics Humans Lactase/*genetics Phenotype Polymorphism, Single Nucleotide/*genetics *Selection (Genetics)JunIn most human populations, the ability to digest lactose contained in milk usually disappears in childhood, but in European-derived populations, lactase activity frequently persists into adulthood (Scrimshaw and Murray 1988). It has been suggested (Cavalli-Sforza 1973; Hollox et al. 2001; Enattah et al. 2002; Poulter et al. 2003) that a selective advantage based on additional nutrition from dairy explains these genetically determined population differences (Simoons 1970; Kretchmer 1971; Scrimshaw and Murray 1988; Enattah et al. 2002), but formal population-genetics-based evidence of selection has not yet been provided. To assess the population-genetics evidence for selection, we typed 101 single-nucleotide polymorphisms covering 3.2 Mb around the lactase gene. In northern European-derived populations, two alleles that are tightly associated with lactase persistence (Enattah et al. 2002) uniquely mark a common (~77%) haplotype that extends largely undisrupted for >1 Mb. We provide two new lines of genetic evidence that this long, common haplotype arose rapidly due to recent selection: (1) by use of the traditional F(ST) measure and a novel test based on p(excess), we demonstrate large frequency differences among populations for the persistence-associated markers and for flanking markers throughout the haplotype, and (2) we show that the haplotype is unusually long, given its high frequency--a hallmark of recent selection. We estimate that strong selection occurred within the past 5,000-10,000 years, consistent with an advantage to lactase persistence in the setting of dairy farming; the signals of selection we observe are among the strongest yet seen for any gene in the genome.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15114531 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't15114531WDivisions of Genetics and Endocrinology, Harvard Medical School, Boston, MA 02115, USA.~?o Blackwelder, W. C. Elston, R. C.1985FA comparison of sib-pair linkage tests for disease susceptibility loci85-97Genet Epidemiol21Alleles Epidemiologic Methods Family Gene Frequency Genetic Diseases, Inborn/*genetics Genetic Markers Humans *Linkage (Genetics) Models, Genetic StatisticsAn analytical study is conducted of the properties of statistical tests to detect linkage between a disease locus and a very polymorphic marker locus when data on sib pairs are available. In most instances the most powerful test is the test based on the mean number of marker alleles shared identical by descent by the two members of a sib pair, and the most efficient sampling strategy is almost always to sample only pairs with both sibs affected. We show it is valid to use the information from all possible sib pairs as though they came from separate families when data on sibships of size three or larger are available, though more power may be obtained if different weights are given to the different sibship sizes.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3863778 X0741-0395 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.3863778~?p Blangero, J.19934Statistical genetic approaches to human adaptability941-66Hum Biol656Adaptation, Physiological/*genetics Animals Female Genotype Humans Male Mathematics *Models, Genetic Multivariate Analysis Papio Pedigree Stress, Psychological/*genetics Variation (Genetics)DecThe genetic determinants of physiological and developmental responses to environmental stress are poorly understood. This has been primarily due to the difficulty of direct measurement of response and the lack of appropriate statistical genetic methods. Here, I present a unified statistical genetic methodology for human adaptability studies that permits evaluation of the inheritance of quantitative trait response to environmental stressors. The foundation of this approach is the mathematical relationship between genotype-environment interaction and the genetic variance of response to environmental challenge. I describe two basic methods that can be used for either discrete or continuous environments. Each method allows for major loci, residual polygenic variation, and genotype-environment interaction at both the major genic and the polygenic levels. The first method is based on multivariate segregation analysis and is appropriate for situations in which data are available for each individual in each environment. The second method is appropriate for the more common case when response to the environment cannot be observed directly. This method is based on an extension of a mixed major locus/variance component model and can be used when singly measured related individuals are observed in different environments. Three example applications using data on lipoprotein variation in pedigreed baboons are provided to show the utility of these methods.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8300087 F0018-7143 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8300087aDepartment of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX 78228-0147.y~?q Blangero, J.2004^Localization and identification of human quantitative trait loci: king harvest has surely come233-40Curr Opin Genet Dev143wGene Frequency Genetic Predisposition to Disease/*genetics Humans *Linkage (Genetics) Quantitative Trait Loci/*geneticsJunThe scientific process of localization and subsequent identification of genes influencing risk of common diseases is still in its infancy. Initial localization of disease-related loci has traditionally been performed using family-based linkage methods to scan the genome. Early pronouncements of the failure of this approach for common diseases were premature and based on comparing suboptimal linkage designs with overly optimistic and empirically unproven association-based designs. On the contrary, substantial recent progress in the positional cloning of genes influencing such complex phenotypes suggests that modern approaches based around a family-based linkage paradigm will be successful. In particular, the rapidly growing emphasis on the analysis of the genetic basis of quantitative correlates of disease risk represents a novel and promising approach in which initial localization is performed using linkage and subsequent identification utilizes association approaches in positional candidate genes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15172664 M0959-437X (Print) Journal Article Research Support, U.S. Gov't, P.H.S. Review15172664Department of Genetics, Southwest Foundation for Biomedical Research, 7620 NW Loop 410, San Antonio, Texas 78227-5301, USA. john@darwin.sfbr.org~?r'Blangero, J. Williams, J. T. Almasy, L.2000?Robust LOD scores for variance component-based linkage analysisS8-14Genet Epidemiol 19 Suppl 1Computer Simulation *Linkage (Genetics) *Lod Score Models, Genetic Probability Quantitative Trait, Heritable *Variation (Genetics)The variance component method is now widely used for linkage analysis of quantitative traits. Although this approach offers many advantages, the importance of the underlying assumption of multivariate normality of the trait distribution within pedigrees has not been studied extensively. Simulation studies have shown that traits with leptokurtic distributions yield linkage test statistics that exhibit excessive Type I error when analyzed naively. We derive analytical formulae relating the deviation from the expected asymptotic distribution of the lod score to the kurtosis and total heritability of the quantitative trait. A simple correction constant yields a robust lod score for any deviation from normality and for any pedigree structure, and effectively eliminates the problem of inflated Type I error due to misspecification of the underlying probability model in variance component-based linkage analysis.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11055364 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11055364~Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas 78245-0549, USA. john@darwin.sfbr.org~?s'Blangero, J. Williams, J. T. Almasy, L.20006Quantitative trait locus mapping using human pedigrees35-62Hum Biol7219Bias (Epidemiology) Chromosome Mapping/*methods *Data Interpretation, Statistical Environment Gene Frequency/genetics Genotype Humans Likelihood Functions Lod Score *Models, Genetic Multivariate Analysis *Pedigree Prevalence *Quantitative Trait, Heritable Reproducibility of Results Variation (Genetics)/*geneticsFebIn the past decade phenomenal progress has been made in molecular and statistical genetic methods for localizing quantitative trait loci. Because of these advances, we can anticipate a long period of active genetic research in which the genes influencing human quantitative variability will be mapped and their effects accurately evaluated. Here, we review the current state of the science in statistical genetic methods for quantitative trait linkage analysis. In particular, we detail a variance component-based framework for localizing quantitative trait loci and for accurately estimating their relative effect sizes. Attention is paid to the optimal design of human family studies for localizing genes of small to moderate effect. In addition, methods and strategies are described for dealing with the most important complications of quantitative variation, including the assessment of genotype x environment interaction and epistasis.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10721613 F0018-7143 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10721613fDepartment of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX 78245-0549, USA.`~?t'Blangero, J. Williams, J. T. Almasy, L.2001;Variance component methods for detecting complex trait loci151-81 Adv Genet42Analysis of Variance *Chromosome Mapping Epidemiology, Molecular Genotype Humans Likelihood Functions *Linkage (Genetics) Models, Theoretical Multivariate Analysis Nuclear Family Phenotype *Quantitative Trait, HeritableVariance component-based linkage analysis has become a major statistical tool for the localization and evaluation of quantitative trait loci influencing complex phenotypes. The variance component approach has many benefits--it can, for example, be used to analyze large pedigrees, and it is able to accommodate multiple loci simultaneously in a true oligogenic model. Important biological phenomena such as genotype-environment interaction and epistasis are also examined easily in a variance component framework. In this chapter, we review the basic statistical features of variance component linkage analysis, with an emphasis on its power and robustness to distributional violations.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11037320 _0065-2660 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S. Review11037320dDepartment of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas 78245, USA.r~?u1Blangero, J. Williams-Blangero, S. Mahaney, M. C.1993oMultivariate genetic analysis of apo AI concentration and HDL subfractions: evidence for major locus pleiotropy617-22Genet Epidemiol106Apolipoprotein A-I/*genetics *Computer Simulation Female Genetic Predisposition to Disease Humans Likelihood Functions Linkage (Genetics) Lipoproteins, HDL/classification/*genetics Male *Models, Genetic Models, Statistical Multivariate Analysis PhenotypesA major locus influencing apolipoprotein AI (apo AI) serum levels was detected using data from the Donner Laboratory Family Study. This locus accounts for 46% of the phenotypic variability in apo AI levels. Multivariate segregation analysis revealed that this major locus also has significant pleiotropic effects on the relative distribution of high density lipoproteins.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8314070 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8314070aDepartment of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX 78228-0147.~?vBlaschke, R. J. Rappold, G.2006-The pseudoautosomal regions, SHOX and disease233-9Curr Opin Genet Dev163uAnimals Chromosomes/*genetics *Disease Disease Susceptibility Homeodomain Proteins/*genetics Humans Mutation/geneticsJunzThe pseudoautosomal regions represent blocks of sequence identity between the mammalian sex chromosomes. In humans, they reside at the ends of the X and Y chromosomes and encompass roughly 2.7 Mb (PAR1) and 0.33 Mb (PAR2). As a major asset of recently available sequence data, our view of their structural characteristics could be refined considerably. While PAR2 resembles the overall sequence composition of the X chromosome and exhibits only slightly elevated recombination rates, PAR1 is characterized by a significantly higher GC content and a completely different repeat structure. In addition, it exhibits one of the highest recombination frequencies throughout the entire human genome and, probably as a consequence of its structural features, displays a significantly faster rate of evolution. It therefore represents an exceptional model to explore the correlation between meiotic recombination and evolutionary forces such as gene mutation and conversion. At least twenty-nine genes lie within the human pseudoautosomal regions, and these genes exhibit 'autosomal' rather than sex-specific inheritance. All genes within PAR1 escape X inactivation and are therefore candidates for the etiology of haploinsufficiency disorders including Turner syndrome (45,X). However, the only known disease gene within the pseudoautosomal regions is the SHORT STATURE HOMEBOX (SHOX) gene, functional loss of which is causally related to various short stature conditions and disturbed bone development. Recent analyses have furthermore revealed that the phosphorylation-sensitive function of SHOX is directly involved in chondrocyte differentiation and maturation.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16650979 I0959-437X (Print) Journal Article Research Support, Non-U.S. Gov't Review16650979uDepartment of Human Molecular Genetics, University of Heidelberg, Im Neuenheimer Feld 366, 69120 Heidelberg, Germany.?wBock, R.D. Vandenberg, S.G19687Components of heritable variation in mental test scores261-278$Progress in Human Behaviour GeneticsS.G. Vandenberg Baltimore, MD John Hopkins_~?xBoehnke, M. Cox, N. J.1997?Accurate inference of relationships in sib-pair linkage studies423-9Am J Hum Genet612Alleles Diabetes Mellitus, Type 2/genetics *Family Gene Frequency Genetic Markers Humans Likelihood Functions *Linkage (Genetics) Markov Chains *Models, Genetic Reproducibility of ResultsAugRelative-pair designs are routinely employed in linkage studies of complex genetic diseases and quantitative traits. Valid application of these methods requires correct specification of the relationships of the pairs. For example, within a sibship, presumed full sibs actually might be MZ twins, half sibs, or unrelated. Misclassification of half-sib pairs or unrelated individuals as full sibs can result in reduced power to detect linkage. When other family members, such as parents or additional siblings, are available, incorrectly specified relationships usually will be detected through apparent incompatibilities with Mendelian inheritance. Without other family members, sibling relationships cannot be determined absolutely, but they still can be inferred probabilistically if sufficient genetic marker data are available. In this paper, we describe a simple likelihood ratio method to infer the true relationship of a putative sibling pair. We explore the number of markers required to accurately infer relationships typically encountered in a sib-pair study, as a function of marker allele frequencies, marker spacing, and genotyping error rate, and we conclude that very accurate inference of relationships can be achieved, given the marker data from even part of a genome scan. We compare our method to related methods of relationship inference that have been suggested. Finally, we demonstrate the value of excluding non-full sibs in a genetic linkage study of non-insulin-dependent diabetes mellitus.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9311748 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9311748aDepartment of Biostatistics, University of Michigan, Ann Arbor 48109-2029, USA. boehnke@umich.edug?y Bollen, K.A.1989*Structural equations with latent variables New York, NYWileyi|7zBollen, K. A. Stine, R. A.1992DBootstrapping Goodness-of-Fit Measures in Structural Equation Models205-229Sociological Methods & Research212covariance-structures testsNovAssessing overall fit is a topic of keen interest to structural equation modelers, yet measuring goodness of fit has been hampered by several factors. First the assumptions that underlie the chi-square tests of model fit often are violated. Second, many fit measures (eg., Bentler and Bonett's [1980] normed fit index) have unknown statistical distributions so that hypothesis testing, confidence intervals, or comparisons of significant differences in these fit indices are not possible. Finally, modelers have little knowledge about the distribution and behavior of the fit measures for misspecified models or for nonnested models. Given this situation, bootstrapping techniques would appear to be an ideal means to tackle these problems. Indeed, Bentler's (1989) EQS 3.0 and Joreskog and Sorbom's (forthcoming) LISREL 8 have bootstrap resampling options to bootstrap fit indices. In this article the authors (a) demonstrate that the usual bootstrapping methods will fail when applied to the original data, (b) explain why this occurs, and (c) propose a modified bootstrap method for the chi-square test statistic for model fit. They include simulated and empirical examples to illustrate their results.://A1992JV15300004.Jv153 Times Cited:55 Cited References Count:21 0049-1241ISI:A1992JV15300004bBollen, Ka Univ N Carolina,Sociol,Chapel Hill,Nc 27514 Univ Penn,Wharton Sch,Philadelphia,Pa 19104English~?{Boomsma, D. I.1996QUsing multivariate genetic modeling to detect pleiotropic quantitative trait loci161-6 Behav Genet262Adult Alleles Child *Chromosome Mapping Computer Simulation Female Gene Frequency Genetic Markers/*genetics Genotype Humans Linkage (Genetics)/*genetics Male *Models, Genetic Pedigree Twins, Dizygotic/genetics Twins, Monozygotic/geneticsMarLarge numbers of sibling pairs or other relatives are needed to detect linkage between a quantitative trait locus (QTL) and a marker, especially if the variance of the QTL is low relative to the total phenotypic variance of the trait. One strategy to increase the power to detect linkage is to reduce the environmental variance in the trait under analysis. This approach was explored by carrying out a series of simulation studies in which multivariate observations were used to estimate individual genotypic values at a QTL, that pleiotropically affected more than one trait. Simulations for different QTL allele frequencies with a completely informative marker showed that the power to detect the QTL increased substantially when estimates of individual genotypic values at the QTL were used in the linkage analysis instead of phenotypic observations. An advantage of this approach is that, rather than employing phenotypic selection, individuals with extreme genotypes may selected when ascertaining a sample of extreme families.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8639151 !0001-8244 (Print) Journal Article8639151]Department of psychonomics, Vrije Universiteit, Amsterdam, The Netherlands. dorret@psy.vu.nl. ~?|sBoomsma, D. I. Beem, A. L. van den Berg, M. Dolan, C. V. Koopmans, J. R. Vink, J. M. de Geus, E. J. Slagboom, P. E.2000<Netherlands twin family study of anxious depression (NETSAD)323-34Twin Res34Adolescent Adult Anxiety/diagnosis/*epidemiology/*genetics/psychology Depression/diagnosis/*epidemiology/*genetics/psychology Factor Analysis, Statistical Female Humans Interview, Psychological Linkage (Genetics) Longitudinal Studies Male Models, Genetic Multivariate Analysis Netherlands/epidemiology Neurotic Disorders/diagnosis/*epidemiology/*genetics/psychology Personality/*genetics Psychiatric Status Rating Scales *Quantitative Trait, Heritable Questionnaires Twins/*genetics/psychologyDecIn a longitudinal study of Dutch adolescent and young adult twins, their parents and their siblings, questionnaire data were collected on depression, anxiety and correlated personality traits, such as neuroticism. Data were collected by mailed surveys in 1991, 1993, 1995 and 1997. A total of 13,717 individuals from 3344 families were included in the study. To localise quantitative trait loci (QTLs) involved in anxiety and depression, the survey data were used to select the most informative families for a genome-wide search. For each individual a genetic factor score was computed, based on a genetic multivariate analysis of anxiety, depression, neuroticism and somatic anxiety. A family was selected if at least two siblings (or DZ twins) had extreme factor scores. Both discordant (high-low) and concordant (high-high and low-low) pairs were included in the selected sample. Once an extreme sibling pair was selected, all family members (parents and additional siblings of the selected pair) who had at least once returned a questionnaire booklet were asked to provide a DNA sample. In total, 2724 individuals from 563 families (1007 parents and 1717 offspring) were approached and 1975 individuals from 479 families (643 patients and 1332 offspring) complied by returning a buccal swab for DNA isolation. All offspring from selected families were asked to participate in a psychiatric interview and in a 24-hour ambulatory assessment of cardiovascular parameters and cortisol. The interview consisted of the WHO-Composite International Diagnostic Interview and was administered to 1253 offspring. In this paper we describe the genetic-epidemiological analyses of the survey data on anxiety, somatic anxiety, neuroticism and depression. We detail how these data were used to select families for the QTL study and discuss strategies that may help elucidate the molecular pathways leading from genes to anxious depression.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11463154 M1369-0523 (Print) Journal Article Research Support, Non-U.S. Gov't Twin Study11463154eDepartment of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands. dorret@psy.vu.nlQ~?}Boomsma, D. I. Dolan, C. V.1998xA comparison of power to detect a QTL in sib-pair data using multivariate phenotypes, mean phenotypes, and factor scores329-40 Behav Genet285Chi-Square Distribution Chromosome Mapping Humans *Models, Genetic Multivariate Analysis *Phenotype *Quantitative Trait, Heritable Social EnvironmentSepThe power to detect a quantitative trait locus (QTL) in sib-pair data is investigated. We assume that we have at our disposal 3 or 4 related phenotypic measures in a sample of sib-pairs. Individual differences in these phenotypes are due to a common QTL and specific (i.e., unique to each phenotype) nonshared environmental and specific polygenic additive effects. In addition, models are considered that include common nonshared environmental effects and/or common polygenic additive effects. We calculate the power to detect the QTL in a genetic covariance structure analysis of the multivariate data, of the mean phenotypic data, and of factor scores. The use of factor scores is shown to be universally more powerful than the use of multivariate or mean phenotypic data. We also investigate the effect of using a single sample of sib-pairs to both calculate the factor score regression matrix and to carry out the QTL analysis. The use of a single sample to both these ends results in a loss of power compared to the theoretical, expected power. The gain in power attributable to the use of factor scores, however, outweighs this observed loss in power. The advantages of using factor scores in selecting sib-pairs are discussed.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9926615 B0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't9926615ZDepartment of Psychology, Vrije Universiteit, Amsterdam, The Netherlands. dorret@psy.vu.nl?~Boomsma, D.I. Dolan, C.V. 2000eMultivariate QTL analysis using structural equation modeling: a look at power under simple conditions203-218&Advances in Twin and Sib-pair Analysis'T.D. Spector H. Snieder A.J. MacgregorLondonGreenwich Medical Media~?Boomsma, D. I. Molenaar, P. C.1986FUsing LISREL to analyze genetic and environmental covariance structure237-50 Behav Genet162^Analysis of Variance *Computers *Environment *Genetics Humans *Models, Genetic *Software TwinsMarehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3755040 !0001-8244 (Print) Journal Article3755040~?.Boomsma, D. I. Molenaar, P. C. Orlebeke, J. F.1990@Estimation of individual genetic and environmental factor scores83-91Genet Epidemiol71Adolescent Blood Pressure/genetics Computer Simulation Confidence Intervals Environment Factor Analysis, Statistical *Genetic Techniques Humans Male Mathematics Models, Genetic Multivariate Analysis Twins/genetics Variation (Genetics)/*geneticssImplicit in the application of the common-factor model as a method for decomposing trait covariance into a genetic and environmental part is the use of factor scores. In multivariate analyses, it is possible to estimate these factor scores for the communal part of the model. Estimation of scores on latent factors in terms of individual observations within the context of a twin/family study amounts to estimation of individual genetic and environmental scores. Such estimates may be of both theoretical and practical interest and may be provided with confidence intervals around the individual estimates. The method is first illustrated with stimulated twin data and next is applied to blood pressure data obtained in a Dutch sample of 59 male adolescent twin pairs. Subjects with high blood pressure can be distinguished into groups with high genetic or high environmental scores.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2184093 !0741-0395 (Print) Journal Article2184093IDepartment of Psychology, Vrije Universiteit, Amsterdam, The Netherlands.~?<Boomsma, D. I. Snieder, H. de Geus, E. J. van Doornen, L. J.1998=Heritability of blood pressure increases during mental stress15-24Twin Res11Adolescent Adult Blood Pressure/*genetics Female Humans Male Mental Processes Reaction Time Rest Stress, Psychological/*physiopathology Twins/*geneticsMaypWe studied the influence of mental stress on the contributions of genes and environment to individual variation in systolic (SBP) and diastolic (DBP) blood pressure by structural equation modelling in 320 adolescent male and female twins. Blood pressure data were collected during rest and during a reaction time and a mental arithmetic task. Univariate analyses of SBP and DBP showed familial aggregation for blood pressure. A genetic explanation for this resemblance was most likely, although during rest conditions a model that attributed familial resemblance to shared environmental factors, also fitted the data. There was no evidence for sex differences in heritabilities. Multivariate analyses showed significant heterogeneity between sexes for the intercorrelations of the blood pressure data measured under different rest and task conditions. Multivariate genetic analyses were therefore carried out separately in males and females. For SBP and DBP in females and for SBP in males an increase in heritabilities was seen for blood pressure measured during stress, as compared to rest measurements. The influence of shared environmental factors decreased during stress. For DBP in males no significant contributions of shared environment were found. The multivariate analyses indicated that the same genetic and environmental influences are expressed during rest and stress conditions.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10051353 M1369-0523 (Print) Journal Article Research Support, Non-U.S. Gov't Twin Study10051353hDepartment of Physiological Psychology, Vrije Universiteit, Amsterdam, The Netherlands. dorret@psy.vu.nl1~?Botstein, D. Risch, N.2003~Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease228-37 Nat Genet33 SupplAlleles Chromosome Mapping Cloning, Molecular Genetic Diseases, Inborn/*genetics/history Genomics/history/trends Genotype History, 20th Century History, 21st Century Humans Linkage Disequilibrium Mutation Phenotype Polymorphism, Single NucleotideMar The past two decades have witnessed an explosion in the identification, largely by positional cloning, of genes associated with mendelian diseases. The roughly 1,200 genes that have been characterized have clarified our understanding of the molecular basis of human genetic disease. The principles derived from these successes should be applied now to strategies aimed at finding the considerably more elusive genes that underlie complex disease phenotypes. The distribution of types of mutation in mendelian disease genes argues for serious consideration of the early application of a genomic-scale sequence-based approach to association studies and against complete reliance on a positional cloning approach based on a map of anonymous single nucleotide polymorphism haplotypes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12610532 `1061-4036 (Print) Historical Article Journal Article Research Support, U.S. Gov't, P.H.S. Review12610532}Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA. Botstein@genome.Stanford.edu~?3Botstein, D. White, R. L. Skolnick, M. Davis, R. W.1980\Construction of a genetic linkage map in man using restriction fragment length polymorphisms314-31Am J Hum Genet323Chromosome Deletion *Chromosome Mapping *Chromosomes, Human DNA/chemical synthesis DNA Restriction Enzymes DNA, Recombinant Genetic Markers Genetic Screening Humans *Linkage (Genetics) Models, Genetic Nucleic Acid Hybridization Pedigree *Polymorphism, GeneticMay!We describe a new basis for the construction of a genetic linkage map of the human genome. The basic principle of the mapping scheme is to develop, by recombinant DNA techniques, random single-copy DNA probes capable of detecting DNA sequence polymorphisms, when hybridized to restriction digests of an individual's DNA. Each of these probes will define a locus. Loci can be expanded or contracted to include more or less polymorphism by further application of recombinant DNA technology. Suitably polymorphic loci can be tested for linkage relationships in human pedigrees by established methods; and loci can be arranged into linkage groups to form a true genetic map of "DNA marker loci." Pedigrees in which inherited traits are known to be segregating can then be analyzed, making possible the mapping of the gene(s) responsible for the trait with respect to the DNA marker loci, without requiring direct access to a specified gene's DNA. For inherited diseases mapped in this way, linked DNA marker loci can be used predictively for genetic counseling.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6247908 M0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S. Review6247908|?Box, G.E.P. Tiao, G.C 1973+Bayesian inference in statistical analysis. Reading, MA.Addison-Wesley ~?FBroman, K. W. Murray, J. C. Sheffield, V. C. White, R. L. Weber, J. L.1998XComprehensive human genetic maps: individual and sex-specific variation in recombination861-9Am J Hum Genet633*Chromosome Mapping Chromosomes, Human, Pair 1 Chromosomes, Human, Pair 14 Chromosomes, Human, Pair 19 Chromosomes, Human, Pair 21 Chromosomes, Human, Pair 4 Chromosomes, Human, Pair 7 Female Genetic Markers *Genome, Human Genomic Imprinting Genotype Humans Male *Polymorphism, Genetic *Recombination, Genetic *Repetitive Sequences, Nucleic Acid Sex Characteristics United States Utah *Variation (Genetics)SepoComprehensive human genetic maps were constructed on the basis of nearly 1 million genotypes from eight CEPH families; they incorporated >8,000 short tandem-repeat polymorphisms (STRPs), primarily from Genethon, the Cooperative Human Linkage Center, the Utah Marker Development Group, and the Marshfield Medical Research Foundation. As part of the map building process, 0.08% of the genotypes that resulted in tight double recombinants and that largely, if not entirely, represent genotyping errors, mutations, or gene-conversion events were removed. The total female, male, and sex-averaged lengths of the final maps were 44, 27, and 35 morgans, respectively. Numerous (267) sets of STRPs were identified that represented the exact same loci yet were developed independently and had different primer pairs. The distributions of the total number of recombination events per gamete, among the eight mothers of the CEPH families, were significantly different, and this variation was not due to maternal age. The female:male ratio of genetic distance varied across individual chromosomes in a remarkably consistent fashion, with peaks at the centromeres of all metacentric chromosomes. The new linkage maps plus much additional information, including a query system for use in the construction of reliably ordered maps for selected subsets of markers, are available from the Marshfield Website.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9718341 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9718341[Marshfield Medical Research Foundation, Marshfield, WI 54449, USA. BromanK@cmg.mfldclin.edu?Bross, I1954"Misclassification in 2 × 2 tables478-486 Biometrics 10Vhttp://links.jstor.org/sici?sici=0006-341X(195412)10%3A4%3C478%3AMI2X2T%3E2.0.CO%3B2-B X~?JBrzustowicz, L. M. Merette, C. Xie, X. Townsend, L. Gilliam, T. C. Ott, J.1993dMolecular and statistical approaches to the detection and correction of errors in genotype databases1137-45Am J Hum Genet535Bias (Epidemiology) Chromosomes, Human, Pair 5 *Databases, Factual/standards Genetic Markers *Genotype Humans Linkage (Genetics) Models, Genetic Molecular Biology Quality Control StatisticsNovjErrors in genotyping data have been shown to have a significant effect on the estimation of recombination fractions in high-resolution genetic maps. Previous estimates of errors in existing databases have been limited to the analysis of relatively few markers and have suggested rates in the range 0.5%-1.5%. The present study capitalizes on the fact that within the Centre d'Etude du Polymorphisme Humain (CEPH) collection of reference families, 21 individuals are members of more than one family, with separate DNA samples provided by CEPH for each appearance of these individuals. By comparing the genotypes of these individuals in each of the families in which they occur, an estimated error rate of 1.4% was calculated for all loci in the version 4.0 CEPH database. Removing those individuals who were clearly identified by CEPH as appearing in more than one family resulted in a 3.0% error rate for the remaining samples, suggesting that some error checking of the identified repeated individuals may occur prior to data submission. An error rate of 3.0% for version 4.0 data was also obtained for four chromosome 5 markers that were retyped through the entire CEPH collection. The effects of these errors on a multipoint map were significant, with a total sex-averaged length of 36.09 cM with the errors, and 19.47 cM with the errors corrected. Several statistical approaches to detect and allow for errors during linkage analysis are presented. One method, which identified families containing possible errors on the basis of the impact on the maximum lod score, showed particular promise, especially when combined with the limited retyping of the identified families. The impact of the demonstrated error rate in an established genotype database on high-resolution mapping is significant, raising the question of the overall value of incorporating such existing data into new genetic maps.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8213837 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.8213837\Department of Psychiatry, Columbia University, College of Physicians and Surgeons, NY 10032.~?8Burdick, J. T. Chen, W. M. Abecasis, G. R. Cheung, V. G.20065In silico method for inferring genotypes in pedigrees1002-4 Nat Genet389Chi-Square Distribution Child Chromosome Mapping *Computer Simulation Genetic Markers *Genotype Humans Internet Linkage (Genetics) *Pedigree Polymerase Chain Reaction Polymorphism, Single NucleotideSepOur genotype inference method combines sparse marker data from a linkage scan and high-resolution SNP genotypes for several individuals to infer genotypes for related individuals. We illustrate the method's utility by inferring over 53 million SNP genotypes for 78 children in the Centre d'Etude du Polymorphisme Humain families. The method can be used to obtain high-density genotypes in different family structures, including nuclear families commonly used in complex disease gene mapping studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16921375 X1061-4036 (Print) Comparative Study Journal Article Research Support, N.I.H., Extramural16921375uDepartment of Pediatrics, University of Pennsylvania, 3516 Civic Center Blvd., Philadelphia, Pennsylvania 19104, USA.n~?QBurgtorf, C. Kepper, P. Hoehe, M. Schmitt, C. Reinhardt, R. Lehrach, H. Sauer, S.2003_Clone-based systematic haplotyping (CSH): a procedure for physical haplotyping of whole genomes2717-24 Genome Res1312fCloning, Molecular/*methods *Genome, Human *Haplotypes Humans Polymorphism, Single Nucleotide/geneticsDecWe present a novel methodology to determine the phase of single-nucleotide polymorphisms (SNPs) on a chromosome, which we term clone-based systematic haplotyping (CSH). The CSH procedure is based on separating the allelic chromosomes of a diploid genome by fosmid/cosmid cloning, and subsequent SNP typing of 96 clone pools, each representing approximately 10% of the genome. The pools are screened by PCR for the sequence of interest, followed by SNP typing on the PCR products using the GOOD assay. We demonstrate that by CSH, the haplotype of SNPs separated by more than 50 kilobases can definitely be assigned. We propose this method as being suitable for constructing maps of ancestral haplotypes, analysis of complex diseases, and for diagnosis of rare defects in which the molecular haplotype is crucial. In addition, by amplifying the initial DNA by many orders of magnitude, the original DNA resource is effectively immortalized, enabling the haplotyping of hundreds of thousands of SNPs per individual.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14656974 B1088-9051 (Print) Journal Article Research Support, Non-U.S. Gov't14656974cMax-Planck-Institute for Molecular Genetics, D-14195 Berlin-Dahlem, Germany. burgtorf@molgen.mpg.de~?Campbell, C. D. Ogburn, E. L. Lunetta, K. L. Lyon, H. N. Freedman, M. L. Groop, L. C. Altshuler, D. Ardlie, K. G. Hirschhorn, J. N.2005>Demonstrating stratification in a European American population868-72 Nat Genet378sEuropean Continental Ancestry Group/*genetics *Genetics, Population Genotype Humans Polymorphism, Single NucleotideAugPPopulation stratification occurs in case-control association studies when allele frequencies differ between cases and controls because of ancestry. Stratification may lead to false positive associations, although this issue remains controversial. Empirical studies have found little evidence of stratification in European-derived populations, but potentially significant levels of stratification could not be ruled out. We studied a European American panel discordant for height, a heritable trait that varies widely across Europe. Genotyping 178 SNPs and applying standard analytical methods yielded no evidence of stratification. But a SNP in the gene LCT that varies widely in frequency across Europe was strongly associated with height (P < 10(-6)). This apparent association was largely or completely due to stratification; rematching individuals on the basis of European ancestry greatly reduced the apparent association, and no association was observed in Polish or Scandinavian individuals. The failure of standard methods to detect this stratification indicates that new methods may be required.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16041375 B1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't16041375~Program in Genomics and Division of Endocrinology, Children's Hospital, 300 Longwood Avenue, Boston, Massachusetts 02115, USA.|7,Cannings, C. Thompson, E. A. Skolnick, M. H.1978*Probability Functions on Complex Pedigrees26-61Advances in Applied Probability101://A1978EX90000014/Ex900 Times Cited:276 Cited References Count:24 0001-8678ISI:A1978EX90000014Cannings, C Univ Sheffield,Sheffield S1 3jd,S Yorkshire,England Univ Cambridge,Cambridge,England Univ Utah,Salt Lake City,Ut 84112English U~? Cantor, R. M.1983[A multivariate genetic analysis of ridge count data from the offspring of monozygotic twins161-207Acta Genet Med Gemellol (Roma)323-4Child *Dermatoglyphics Factor Analysis, Statistical Female *Genotype Humans Male Phenotype Pregnancy Sex Factors *Twins *Twins, Monozygotic Variation (Genetics)c Two methods have been studied for extending the half-sib model, which was developed by Nance and Corey [34] for the genetic analysis of univariate traits, to include the analysis of multivariate traits. The methods are adaptations of the Bock and Vandenberg procedure [4] and the form of confirmatory maximum likelihood factor analysis which was developed by Joreskog and Sorbom for the LISREL IV program [22]. These methods were applied to sex-adjusted individual finger ridge count data from the offspring of monozygotic twins. The Bock and Vandenberg procedure was applied to the eigenvalues and eigenvectors from a nested analysis of variance on 30 balanced male twin kinships. The result was a matrix of pure genetic effects which was at least positive semidefinite, and therefore appropriate for factor analytic procedures. Principal components analysis revealed two substantial genetic factors, one with a strong impact on the ridge counts of all ten fingers, with the largest loadings on the three central fingers of each hand, and the other influencing the thumbs and fifth fingers with opposite signs. In both cases, the factor loadings of homologous fingers were nearly equal. Employing the Bock and Vandenberg procedure to analyze multivariate data from MZ twin kinship has both positive and negative features. Its greatest strength is that it is easy to program with the Nested, Matrix, and Factor Procedures of the Statistical Analysis System package [see 44]. Multivariate half-sib data on any traits can be quickly explored for genotypic associations with the availability of this package or others like it. The exploratory findings from this analysis, the LISREL analysis, and the Bock and Vandenberg analysis on MZ and DZ twin pairs, as reported by Nance et al [36] are in agreement. This attests to the validity of the results from the two procedures. Such strong agreement may not be the case for other genetic structures, however, and analysis of other sets of traits or analyses on simulated data should clarify the cases under which the procedures produce concordant results. A negative feature of the Bock and Vandenberg procedure is that it wastes much of the data taken from individuals who attend the Twin Clinic. Both male and female kinships are routinely ascertained, with equal frequencies, although in this analysis we have examined the results from male kinships only. In addition, a substantial number of the male kinships either do not meet the criterion of at least two individuals in each sibship, or else have larger sibships containing individuals who must be excluded from the analysis.(ABSTRACT TRUNCATED AT 400 WORDS)ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6687028 !0001-5660 (Print) Journal Article6687028M~?Cardon, L. R. Abecasis, G. R.20036Using haplotype blocks to map human complex trait loci135-40 Trends Genet193Chromosome Mapping/*methods Forecasting Gene Frequency Genetic Markers Genome, Human Genotype *Haplotypes Humans Linkage Disequilibrium Models, Genetic Models, Statistical Polymorphism, Single Nucleotide *Quantitative Trait, Heritable Recombination, Genetic Variation (Genetics)Mar:Understanding of linkage disequilibrium (LD) in human populations could facilitate the discovery of genes that influence complex human diseases. The "HapMap" project is now underway to characterize patterns of LD in the human genome. A pilot study showed "haplotype blocks" in 51 regions scattered throughout the genome. These intriguing results raise important questions about the nature of recombination, and highlight practical issues of marker collection, the influence of statistical modelling on apparent block structure, and the levels of genotyping necessary for studies of common diseases. Knowledge of local disequilibrium patterns may help identify common polymorphisms involved in complex disease, but completely new analytical methods and experimental designs will be required to identify important rare variants.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12615007 n0168-9525 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Review12615007}Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. lon.cardon@well.ox.ac.uk~?Cardon, L. R. Bell, J. I.2001.Association study designs for complex diseases91-9 Nat Rev Genet22@Genetic Diseases, Inborn/*genetics Humans Linkage DisequilibriumFebAssessing the association between DNA variants and disease has been used widely to identify regions of the genome and candidate genes that contribute to disease. However, there are numerous examples of associations that cannot be replicated, which has led to skepticism about the utility of the approach for common conditions. With the discovery of massive numbers of genetic markers and the development of better tools for genotyping, association studies will inevitably proliferate. Now is the time to consider critically the design of such studies, to avoid the mistakes of the past and to maximize their potential to identify new components of disease.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11253062 n1471-0056 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Review11253062vUniversity of Oxford, Nuffield Department of Clinical Medicine, Headington, Oxford OX3 9DU, UK. john.bell@ndm.ox.ac.uk~?Cardon, L. R. Fulker, D. W.1994RThe power of interval mapping of quantitative trait loci, using selected sib pairs825-33Am J Hum Genet554Algorithms Alleles Gene Frequency Genes, Recessive Genetics, Medical/*methods Genotype Heterozygote Detection Humans Mathematics *Models, GeneticOctThe interval-mapping procedure of Fulker and Cardon for analysis of a quantitative-trait loci (QTL) is extended for application to selected samples of sib pairs. Phenotypic selection of sib pairs, which is known to yield striking increases in power when a single marker is used, provides further increases in power when the interval-mapping approach is used. The greatest benefits of the combined approach are apparent with coarse maps, where QTLs of relatively modest (15%-20%) heritability can be detected with widely spaced markers (40-60 cM apart) in reasonably sized sibling samples. Useful information concerning QTL location is afforded by interval mapping in both selected and unselected samples.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7942859 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.79428599Department of Mathematics, Stanford University, CA 94305.~?Cardon, L. R. Palmer, L. J.2003:Population stratification and spurious allelic association598-604Lancet3619357*Alleles Case-Control Studies *Genetics, Population Humans *Molecular Biology *Pharmacogenetics Phenotype Polymorphism, GeneticFeb 15Great efforts and expense have been expended in attempts to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, technology for detection and scoring of single nucleotide polymorphisms (SNPs) has undergone rapid development, extensive catalogues of SNPs across the genome have been constructed, and SNPs have been increasingly used as a means for investigation of the genetic causes of complex human diseases. For many diseases, population-based studies of unrelated individuals--in which case-control and cohort studies serve as standard designs for genetic association analysis--can be the most practical and powerful approach. However, extensive debate has arisen about optimum study design, and considerable concern has been expressed that these approaches are prone to population stratification, which can lead to biased or spurious results. Over the past decade, a great shift has been noted, away from case-control and cohort studies, towards family-based association designs. These designs have fewer problems with population stratification but have greater genotyping and sampling requirements, and data can be difficult or impossible to gather. We discuss past evidence for population stratification on genotype-phenotype association studies, review methods to detect and account for it, and present suggestions for future study design and analysis.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12598158 n0140-6736 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Review12598158mWellcome Trust Centre for Human Genetics, University of Oxford, OX3 7BN, Oxford, UK. Ion.cardon@well.ox.ac.uk ~? Carey, G.2005Cholesky problems653-65 Behav Genet355Algorithms Animals *Chi-Square Distribution Female Genetics, Behavioral/*methods/statistics & numerical data Humans Male *Models, Genetic *Models, Statistical Twin Studies/statistics & numerical data Twins/genetics *Variation (Genetics)Sep Behavioral geneticists commonly parameterize a genetic or environmental covariance matrix as the product of a lower diagonal matrix postmultiplied by its transpose-a technique commonly referred to as "fitting a Cholesky." Here, simulations demonstrate that this procedure is sometimes valid, but at other times: (1) may not produce fit statistics that are distributed as a chi2; or (2) if the distribution of the fit statistic is chi2, then the degrees of freedom (df) are not always the difference between the number of parameters in the general model less the number of parameters in a constrained model. It is hypothesized that the problem is related to the fact that the Cholesky parameterization requires that the covariance matrix formed by the product be either positive definite or singular. Even though a population covariance matrix may be positive definite, the combination of sampling error and the derived--as opposed to directly observed--nature of genetic and environmental matrices allow matrices that are negative (semi) definite. When this occurs, fitting a Cholesky constrains the numerical area of search and compromises the maximum likelihood theory currently used in behavioral genetics. Until the reasons for this phenomenon are understood and satisfactory solutions are developed, those who fit Cholesky matrices face the burden of demonstrating the validity of their fit statistics and the df for model comparisons. An interim remedy is proposed--fit an unconstrained model and a Cholesky model, and if the two differ, then report the difference in fit statistics and parameter estimates. Cholesky problems are a matter of degree, not of kind. Thus, some Cholesky solutions will differ trivially from the unconstrained solutions, and the importance of the problems must be assessed by how often the two lead to different substantive interpretation of the results. If followed, the proposed interim remedy will develop a body of empirical data to assess the extent to which Cholesky problems are important substantive issues versus statistical curiosities.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16184491 F0001-8244 (Print) Journal Article Research Support, N.I.H., Extramural16184491Department of Psychology and Institute for Behavioral Genetics, University of Colorado, Boulder, CO 80309-0345, USA. gregory.carey@colorado.edu~?Carey, G. Williamson, J.1991RLinkage analysis of quantitative traits: increased power by using selected samples786-96Am J Hum Genet494Gene Frequency/genetics Genes, Recessive/genetics Genotype Humans Linkage (Genetics)/*genetics Mathematics Models, Statistical Phenotype Regression Analysis *Sampling Studies Variation (Genetics)/*geneticsOctAlthough a number of methods have been developed for linkage analysis of quantitative traits, power is relatively poor unless there is a single major locus of very large effect. Here it is demonstrated that the use of selected samples (i.e., ascertainment of a proband with an extreme score on the quantitative measure) can dramatically increase power, especially when proband selection is performed on the tail of a distribution with an infrequent recessive gene. Depending on gene action and allele frequency, selected samples permit detection of a major locus that accounts for as little as 10%-20% of the phenotypic variation. The judicious use of selected samples can make an appreciable difference in the feasibility of linkage studies for quantitative traits.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1897525 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.1897525NInstitute for Behavioral Genetics, University of Colorado, Boulder 80309-0447. C~?OCarlson, C. S. Eberle, M. A. Rieder, M. J. Yi, Q. Kruglyak, L. Nickerson, D. A.2004~Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium106-20Am J Hum Genet741African Continental Ancestry Group Algorithms European Continental Ancestry Group Genetic Diseases, Inborn/*genetics Homozygote Humans Linkage Disequilibrium/*genetics Polymorphism, Single Nucleotide/*genetics United StatesJanCommon genetic polymorphisms may explain a portion of the heritable risk for common diseases. Within candidate genes, the number of common polymorphisms is finite, but direct assay of all existing common polymorphism is inefficient, because genotypes at many of these sites are strongly correlated. Thus, it is not necessary to assay all common variants if the patterns of allelic association between common variants can be described. We have developed an algorithm to select the maximally informative set of common single-nucleotide polymorphisms (tagSNPs) to assay in candidate-gene association studies, such that all known common polymorphisms either are directly assayed or exceed a threshold level of association with a tagSNP. The algorithm is based on the r(2) linkage disequilibrium (LD) statistic, because r(2) is directly related to statistical power to detect disease associations with unassayed sites. We show that, at a relatively stringent r(2) threshold (r2>0.8), the LD-selected tagSNPs resolve >80% of all haplotypes across a set of 100 candidate genes, regardless of recombination, and tag specific haplotypes and clades of related haplotypes in nonrecombinant regions. Thus, if the patterns of common variation are described for a candidate gene, analysis of the tagSNP set can comprehensively interrogate for main effects from common functional variation. We demonstrate that, although common variation tends to be shared between populations, tagSNPs should be selected separately for populations with different ancestries.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14681826 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.14681826hDepartment of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. csc47@u.washington.edu?+Cavalli-Sforza, L.L. Menozzi, P. Piazza, A.1994$History and Geography of Human Genes Princeton, NJPrinceton University Press~?7Chapman, J. M. Cooper, J. D. Todd, J. A. Clayton, D. G.2003Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power18-31 Hum Hered561-3*Data Interpretation, Statistical *Genetic Predisposition to Disease *Haplotypes Humans *Linkage Disequilibrium Multivariate AnalysisIn the 'indirect' method of detecting genetic associations between a trait and a DNA variant, we type several markers in a gene or chromosome region of linkage disequilibrium. If there is association between markers and the trait, we presume the existence of one or more causal polymorphisms in the region. In order to obtain a sufficiently dense set of markers it will almost always be necessary to use single nucleotide polymorphisms (SNPs). Although there is an emerging literature on methods for choosing an optimal set of 'haplotype tag SNPs' (htSNPs) to detect association between a genetic region and a trait, less attention has been given to the problem of how such studies should be analysed when completed, and how the initial data which was used to select the htSNPs should be incorporated into the analysis. This paper discusses this problem for both population- and family-based association studies. The role of the R2 measure of association between a causal locus and various methods of scoring of marker haplotypes is highlighted. In most cases, the simplest method of scoring (locus coding), which does not require phase resolution, is shown generally to be more powerful than scoring methods that include haplotype information. A new 'multi-locus TDT' is also proposed.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14614235 B0001-5652 (Print) Journal Article Research Support, Non-U.S. Gov't14614235JDRF/WT Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. Y~?Chen, W. M. Abecasis, G. R.2006NEstimating the power of variance component linkage analysis in large pedigrees471-84Genet Epidemiol306*Chromosome Mapping Computer Simulation Female Humans *Likelihood Functions *Linkage (Genetics) Male Models, Genetic Models, Statistical Pedigree Phenotype *Quantitative Trait LociSepVariance component linkage analysis is commonly used to map quantitative trait loci (QTLs) in general pedigrees. Large pedigrees are especially attractive for these studies because they provide greater power per genotyped individual than small pedigrees. We propose accurate and computationally efficient methods to calculate the analytical power of variance component linkage analysis that can accommodate large pedigrees. Our analytical power computation involves the approximation of the noncentrality parameter for the likelihood-ratio test by its Taylor expansions. We develop efficient algorithms to compute the second and third moments of the identical by descent (IBD) sharing distribution and enable rapid computation of the Taylor expansions. Our algorithms take advantage of natural symmetries in pedigrees and can accurately analyze many large pedigrees in a few seconds. We verify the accuracy of our power calculation via simulation in pedigrees with 2-5 generations and 2-8 siblings per sibship. We apply this proposed analytical power calculation to 98 quantitative traits in a cohort study of 6,148 Sardinians in which the largest pedigree includes 625 phenotyped individuals. Simulations based on eight representative traits show that the difference between our analytical estimation of the expected LOD score and the average of simulated LOD scores is less than 0.05 (1.5%). Although our analytical calculations are for a fully informative marker locus, in the settings we examined power was similar to what could be attained with a single nucleotide polymorphism (SNP) mapping panel (with >1 SNP/cM). Our algorithms for power analysis together with polygenic analysis are implemented in a freely available computer program, POLY.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16685720 F0741-0395 (Print) Journal Article Research Support, N.I.H., Extramural16685720_Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA. wechen@umich.eduG~?FCherny, S. S. Abecasis, G. R. Cookson, W. O. Sham, P. C. Cardon, L. R.2001dThe effect of genotype and pedigree error on linkage analysis: analysis of three asthma genome scansS117-22Genet Epidemiol 21 Suppl 1Adult Asthma/epidemiology/*genetics Bias (Epidemiology) Child Chromosome Mapping/*statistics & numerical data Female Genetic Screening Genetics, Population *Genotype Humans Male Microsatellite Repeats/genetics *Pedigree6The effects of genotype and relationship errors on linkage results are evaluated in three of the Genetic Analysis Workshop 12 asthma genome scans. A number of errors are detected in the samples. While the evidence for linkage is not striking in any data set with or without error, in some cases the difference in test statistic could support different conclusions. The results provide empirical evidence for the predicted effects of genotype and relationship error and highlight the need for rigorous detection and elimination of data error in complex trait studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11793653 g0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11793653dWellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.k~?QCheung, V. G. Spielman, R. S. Ewens, K. G. Weber, T. M. Morley, M. Burdick, J. T.2005UMapping determinants of human gene expression by regional and genome-wide association1365-9Nature4377063 Alleles Chromatin Immunoprecipitation Gene Expression Regulation/*genetics Genetic Markers/genetics Genetic Predisposition to Disease/genetics *Genome, Human Haplotypes Humans Phenotype Polymorphism, Single Nucleotide/*genetics RNA Polymerase II/immunology/metabolismOct 27To study the genetic basis of natural variation in gene expression, we previously carried out genome-wide linkage analysis and mapped the determinants of approximately 1,000 expression phenotypes. In the present study, we carried out association analysis with dense sets of single-nucleotide polymorphism (SNP) markers from the International HapMap Project. For 374 phenotypes, the association study was performed with markers only from regions with strong linkage evidence; these regions all mapped close to the expressed gene. For a subset of 27 phenotypes, analysis of genome-wide association was performed with >770,000 markers. The association analysis with markers under the linkage peaks confirmed the linkage results and narrowed the candidate regulatory regions for many phenotypes with strong linkage evidence. The genome-wide association analysis yielded highly significant results that point to the same locations as the genome scans for about 50% of the phenotypes. For one candidate determinant, we carried out functional analyses and confirmed the variation in cis-acting regulatory activity. Our findings suggest that association studies with dense SNP maps will identify susceptibility loci or other determinants for some complex traits or diseases.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16251966 1476-4687 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.16251966wDepartment of Pediatrics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. vcheung@mail.med.upenn.edu~? Chotai, J.1984+On the lod score method in linkage analysis359-78 Ann Hum Genet48Pt 48Humans *Linkage (Genetics) *Lod Score Methods StatisticsOct>Genetic epidemiology deals with the interaction of environmental and genetic determinants in common diseases. Linkage analysis is an important branch of this field. The current practice of claiming linkage between two genetic loci when the maximum lod score z(theta) exceeds 3 has not received theoretical justification, whether considered as a sequential or as a fixed sample size test. Within the framework of significance testing, Wald's (1947) formulae are not applicable to allow this procedure a sequential interpretation. Considered as a fixed sample size test, we find that a chi 2 approximation would instead be very adequate. Since repeated significance testing is performed on linkage data, the nominal significance level should be more stringent for each test than the overall level. Some recent developments in group sequential trials by Pocock (1977) and in repeated significance testing by Woodroofe (1979) seem to indicate that the critical value of the maximum lod score should lie roughly between 0.9 and 3.3, depending on the maximum number of repetitions anticipated, on whether the significance level is desired to be 0.05, 0.01 or 0.001, and on whether the test is derived from a one-sided or a two-sided consideration. In terms of the group sequential approach, if a maximum of twenty repetitions is allowed, if z(theta) greater than log10 A is considered as a one-sided test and assumed to be symmetric when linkage is absent, then the type I error is approximately given by 1/A. We also treat the confidence interval approach for exclusion of unlikely recombination values.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6497351 !0003-4800 (Print) Journal Article6497351~?Churchill, G. A. Doerge, R. W.19949Empirical threshold values for quantitative trait mapping963-71Genetics1383k*Chromosome Mapping Crosses, Genetic Genetic Markers Models, Genetic Plants/genetics Recombination, GeneticNovThe detection of genes that control quantitative characters is a problem of great interest to the genetic mapping community. Methods for locating these quantitative trait loci (QTL) relative to maps of genetic markers are now widely used. This paper addresses an issue common to all QTL mapping methods, that of determining an appropriate threshold value for declaring significant QTL effects. An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand. The method is demonstrated using two real data sets derived from F2 and recombinant inbred plant populations. An example using simulated data from a backcross design illustrates the effect of marker density on threshold values.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7851788 J0016-6731 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S.7851788<Biometrics Unit, Cornell University, Ithaca, New York 14853.~? Clark, A. G.1990IInference of haplotypes from PCR-amplified samples of diploid populations111-22 Mol Biol Evol72Algorithms Alleles Animals Base Sequence DNA/genetics *Diploidy Drosophila melanogaster/genetics Haplotypes/*genetics Models, Genetic Polymerase Chain Reaction Polymorphism, Genetic ProbabilityMarDirect sequencing of genomic DNA from diploid individuals leads to ambiguities on sequencing gels whenever there is more than one mismatching site in the sequences of the two orthologous copies of a gene. While these ambiguities cannot be resolved from a single sample without resorting to other experimental methods (such as cloning in the traditional way), population samples may be useful for inferring haplotypes. For each individual in the sample that is homozygous for the amplified sequence, there are no ambiguities in the identification of the allele's sequence. The sequences of other alleles can be inferred by taking the remaining sequence after "subtracting off" the sequencing ladder of each known site. Details of the algorithm for extracting allelic sequences from such data are presented here, along with some population-genetic considerations that influence the likelihood for success of the method. The algorithm also applies to the problem of inferring haplotype frequencies of closely linked restriction-site polymorphisms.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2108305 M0737-4038 (Print) Journal Article Research Support, U.S. Gov't, P.H.S. Review2108305LDepartment of Biology, Pennsylvania State University, University Park 16802.~? Clark, A. G.20040The role of haplotypes in candidate gene studies321-33Genet Epidemiol274Genetic Predisposition to Disease/*epidemiology *Genetics, Population Genome, Human *Haplotypes Humans Linkage Disequilibrium Models, Genetic Mutation Polymorphism, Single Nucleotide Quantitative Trait, Heritable Sequence Analysis, DNA Variation (Genetics)DecWHuman geneticists working on systems for which it is possible to make a strong case for a set of candidate genes face the problem of whether it is necessary to consider the variation in those genes as phased haplotypes, or whether the one-SNP-at-a-time approach might perform as well. There are three reasons why the phased haplotype route should be an improvement. First, the protein products of the candidate genes occur in polypeptide chains whose folding and other properties may depend on particular combinations of amino acids. Second, population genetic principles show us that variation in populations is inherently structured into haplotypes. Third, the statistical power of association tests with phased data is likely to be improved because of the reduction in dimension. However, in reality it takes a great deal of extra work to obtain valid haplotype phase information, and inferred phase information may simply compound the errors. In addition, if the causal connection between SNPs and a phenotype is truly driven by just a single SNP, then the haplotype-based approach may perform worse than the one-SNP-at-a-time approach. Here we examine some of the factors that affect haplotype patterns in genes, how haplotypes may be inferred, and how haplotypes have been useful in the context of testing association between candidate genes and complex traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15368617 M0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S. Review15368617pDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA. ac347@cornell.edu~? Clayton, D.1999]A generalization of the transmission/disequilibrium test for uncertain-haplotype transmission1170-7Am J Hum Genet654Adult Child Chromosome Mapping/*methods/*statistics & numerical data Genetic Markers/genetics Genotype Haplotypes/*genetics Humans Likelihood Functions Linkage Disequilibrium/*genetics Matched-Pair Analysis Nuclear FamilyOctA new transmission/disequilibrium-test statistic is proposed for situations in which transmission is uncertain. Such situations arise when transmission of a multilocus marker haplotype is considered, since haplotype phase is often unknown in a substantial number of instances. Even for single-locus markers, transmission is uncertain if one or both parents are missing. In both these situations, uncertainty may be reduced by the typing of further siblings, whose disease status may be unaffected or unknown. The proposed test is a score test based on a partial score function that omits the terms most influenced by hidden population stratification.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10486336 !0002-9297 (Print) Journal Article10486336nMRC Biostatistics Unit, Institute of Public Health, Cambridge, United Kingdom. david.clayton@mrc-bsu.cam.ac.uk~?"Clayton, D. Chapman, J. Cooper, J.2004HUse of unphased multilocus genotype data in indirect association studies415-28Genet Epidemiol274Genetic Markers Genetic Predisposition to Disease/*epidemiology *Genetics, Population Genotype *Haplotypes Humans Linkage Disequilibrium/*genetics *Models, Genetic Models, StatisticalDec\It is usually assumed that detection of a disease susceptability gene via marker polymorphisms in linkage disequilibrium with it is facilitated by consideration of marker haplotypes. However, capture of the marker haplotype information requires resolution of gametic phase, and this must usually be inferred statistically. Recently, we questioned the value of the marker haplotype information, and suggested that certain analyses of multivariate marker data, not based on haplotypes explicitly and not requiring resolution of gametic phase, are often more powerful than analyses based on haplotypes. Here, we review this work and assess more carefully the situations in which our conclusions might apply. We also relate these analyses to alternative approaches to haplotype analysis, namely those based on haplotype similarity and those inspired by cladistics.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15481099 B0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't15481099Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 2XY, UK.|7 Cohen, J.1992A Power Primer155-159Psychological Bulletin1121JulOne possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for.80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.://A1992JB405000080Jb405 Times Cited:2067 Cited References Count:10 0033-2909ISI:A1992JB40500008ECohen, J Nyu,Dept Psychol,6 Washington Pl,5th Floor,New York,Ny 10003English~?.Colhoun, H. M. McKeigue, P. M. Davey Smith, G.2003@Problems of reporting genetic associations with complex outcomes865-72Lancet3619360Alleles Coronary Disease/genetics *Genetics, Population Humans Polymorphism, Genetic *Publication Bias Research Design Variation (Genetics)/*geneticsMar 8`Inability to replicate many results has led to increasing scepticism about the value of simple association study designs for detection of genetic variants contributing to common complex traits. Much attention has been drawn to the problems that might, in theory, bedevil this approach, including confounding from population structure, misclassification of outcome, and allelic heterogeneity. Other researchers have argued that absence of replication may indicate true heterogeneity in gene-disease associations. We suggest that the most important factors underlying inability to replicate these associations are publication bias, failure to attribute results to chance, and inadequate sample sizes, problems that are all rectifiable. Without changes to present practice, we risk wastage of scientific effort and rejection of a potentially useful research strategy.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12642066 (0140-6736 (Print) Journal Article Review12642066Department of Epidemiology and Public Health, University College London, WC1E 6BT, London, UK. h.colhoun@public-health.ucl.ac.uk}~?Cordell, H. J.2002^Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans2463-8 Hum Mol Genet1120<*Data Interpretation, Statistical *Epistasis, Genetic HumansOct 1Epistasis, the interaction between genes, is a topic of current interest in molecular and quantitative genetics. A large amount of research has been devoted to the detection and investigation of epistatic interactions. However, there has been much confusion in the literature over definitions and interpretations of epistasis. In this review, we provide a historical background to the study of epistatic interaction effects and point out the differences between a number of commonly used definitions of epistasis. A brief survey of some methods for detecting epistasis in humans is given. We note that the degree to which statistical tests of epistasis can elucidate underlying biological interactions may be more limited than previously assumed.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12351582 I0964-6906 (Print) Journal Article Research Support, Non-U.S. Gov't Review12351582University of Cambridge, Department of Medical Genetics, JDRF/WT Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, Addenbrooke's Hospital, Cambridge, CB2 2XY, UK. heather.cordell@cimr.cam.ac.uk D~?Cordell, H. J.2006Estimation and testing of genotype and haplotype effects in case-control studies: comparison of weighted regression and multiple imputation procedures259-75Genet Epidemiol303UCase-Control Studies *Genetic Techniques *Genotype *Haplotypes Humans Odds Ratio RiskAprA popular approach for testing and estimating genotype and haplotype effects associated with a disease outcome is to conduct a population-based case/control study, in which haplotypes are not directly observed but may be inferred probabilistically from unphased genotype data. A variety of methods exist to analyse the resulting data while accounting for the uncertainty in haplotype assignment, but most focus on the issue of testing the global null hypothesis that no genotype or haplotype effects exist. A more interesting question, once a region of disease association has been identified, is to estimate the relevant genotypic or haplotypic effects and to perform tests of complex null hypotheses such as the hypothesis that some loci, but not others, are associated with disease. Here I examine the assumptions behind, and the performance of, two classes of methods for addressing this question. The first is a weighted regression approach in which posterior probabilities of haplotype assignments are used as weights in a logistic regression analysis, generating a test based on either a weighted pseudo-likelihood, or a weighted log-likelihood. The second is a multiple imputation approach using either an improper procedure in which the posterior probabilities are used to generate replicate imputed data sets, or a proper data augmentation procedure. I compare these approaches to a simple expectation substitution (haplotype trend regression) approach. In simulations, all methods gave unbiased parameter estimation but the weighted pseudo-likelihood, expectation substitution and multiple imputation methods had superior confidence interval coverage. For the weighted pseudo-likelihood and expectation substitution methods it was necessary to estimate posterior haplotype assignment probabilities using the combined case/control data, whereas for the multiple imputation approaches it was necessary to estimate these probabilities in the case and control groups separately. Overall, multiple imputation was easiest approach to implement in standard statistical software and to extend to more complex models such as those that include gene-gene or gene-environment interactions.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16496312 T0741-0395 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't16496312Department of Medical Genetics, University of Cambridge, Cambridge Institute for Medical Research, Addenbrooke's Hospital, Cambridge, UK. heather.cordell@cimr.cam.ac.uk `~?Cordell, H. J. Clayton, D. G.2002A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes124-41Am J Hum Genet701 Alleles Case-Control Studies Diabetes Mellitus, Type 1/*genetics Genetic Predisposition to Disease/*genetics HLA-D Antigens/*genetics Haplotypes/genetics Humans Likelihood Functions Logistic Models Models, Genetic Mutation/genetics Nuclear Family Phenotype Polymorphism, Genetic/*geneticsJanA stepwise logistic-regression procedure is proposed for evaluation of the relative importance of variants at different sites within a small genetic region. By fitting statistical models with main effects, rather than modeling the full haplotype effects, we generate tests, with few degrees of freedom, that are likely to be powerful for detecting primary etiological determinants. The approach is applicable to either case/control or nuclear-family data, with case/control data modeled via unconditional and family data via conditional logistic regression. Four different conditioning strategies are proposed for evaluation of effects at multiple, closely linked loci when family data are used. The first strategy results in a likelihood that is equivalent to analysis of a matched case/control study with each affected offspring matched to three pseudocontrols, whereas the second strategy is equivalent to matching each affected offspring with between one and three pseudocontrols. Both of these strategies require you be able to infer parental phase (i.e., those haplotypes present in the parents). Families in which phase cannot be determined must be discarded, which can considerably reduce the effective size of a data set, particularly when large numbers of loci that are not very polymorphic are being considered. Therefore, a third strategy is proposed in which knowledge of parental phase is not required, which allows those families with ambiguous phase to be included in the analysis. The fourth and final strategy is to use conditioning method 2 when parental phase can be inferred and to use conditioning method 3 otherwise. The methods are illustrated using nuclear-family data to evaluate the contribution of loci in the HLA region to the development of type 1 diabetes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11719900 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't11719900rDepartment of Medical Genetics, University of Cambridge, Cambridge, United Kingdom. heather.cordell@cimr.cam.ac.uk~?4Cordell, H. J. Kawaguchi, Y. Todd, J. A. Farrall, M.1995=An extension of the Maximum Lod Score method to X-linked loci435-49 Ann Hum Genet59Pt 4Alleles Diabetes Mellitus, Type 1/*genetics Female Genetic Markers/genetics Humans Linkage (Genetics)/*genetics *Lod Score Male Models, Genetic Pedigree Sex Factors X Chromosome/*geneticsOct,The Maximum Lod Score method for affected relative-pair analysis, introduced by Risch, is a powerful method for detecting linkage between an autosomal marker locus and disease. In order to use the method to detect linkage to markers on the X-chromosome, some modification is necessary. Here we extend the method to be applicable to X-chromosomal data, and derive genetic restrictions on the haplotype-sharing probabilities analogous to the 'possible triangle' restrictions described by Holmans for the autosomal case. Size criteria are derived using asymptotic theory and simulation, and the power is calculated for a number of possible underlying models. The method is applied to data from 284 type 1 diabetic families and evidence is found for the presence of one or more diabetogenic loci on the X-chromosome.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8579335 B0003-4800 (Print) Journal Article Research Support, Non-U.S. Gov't8579335_Nuffield Department of Surgery, Wellcome Trust Centre for Human Genetics, University of Oxford. ~?wCornes, B. K. Medland, S. E. Ferreira, M. A. Morley, K. I. Duffy, D. L. Heijmans, B. T. Montgomery, G. W. Martin, N. G.2005pSex-limited genome-wide linkage scan for body mass index in an unselected sample of 933 Australian twin families616-32Twin Res Hum Genet86*Body Mass Index Chromosomes, Human/*genetics Diabetes Mellitus, Type 2/genetics Genome, Human/*genetics Humans *Linkage (Genetics) Male Obesity/genetics Quantitative Trait Loci/*genetics Sex Factors Twins/*geneticsDecnGenes involved in pathways regulating body weight may operate differently in men and women. To determine whether sex-limited genes influence the obesity-related phenotype body mass index (BMI), we have conducted a general nonscalar sex-limited genome-wide linkage scan using variance components analysis in Mx (Neale, 2002). BMI measurements and genotypic data were available for 2053 Australian female and male adult twins and their siblings from 933 families. Clinical measures of BMI were available for 64.4% of these individuals, while only self-reported measures were available for the remaining participants. The mean age of participants was 39.0 years of age (SD 12.1 years). The use of a sex-limited linkage model identified areas on the genome where quantitative trait loci (QTL) effects differ between the sexes, particularly on chromosome 8 and 20, providing us with evidence that some of the genes responsible for BMI may have different effects in men and women. Our highest linkage peak was observed at 12q24 (-log10p = 3.02), which was near the recommended threshold for suggestive linkage (-log10p = 3.13). Previous studies have found evidence for a quantitative trait locus on 12q24 affecting BMI in a wide range of populations, and candidate genes for noninsulin-dependent diabetes mellitus, a consequence of obesity, have also been mapped to this region. We also identified many peaks near a -log10p of 2 (threshold for replicating an existing finding) in many areas across the genome that are within regions previously identified by other studies, as well as in locations that harbor genes known to influence weight regulation.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16363087 g1832-4274 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16363087sGenetic Epidemiology, Queensland Institute of Medical Research, Post Office Royal Brisbane Hospital, QLD, Australia~?3Cottingham, R. W., Jr. Idury, R. M. Schaffer, A. A.1993.Faster sequential genetic linkage computations252-63Am J Hum Genet531DAlgorithms Female Humans *Linkage (Genetics) Male Pedigree *SoftwareJulLLinkage analysis using maximum-likelihood estimation is a powerful tool for locating genes. As available data sets have grown, the computation required for analysis has grown exponentially and become a significant impediment. Others have previously shown that parallel computation is applicable to linkage analysis and can yield order-of-magnitude improvements in speed. In this paper, we demonstrate that algorithmic modifications can also yield order-of-magnitude improvements, and sometimes much more. Using the software package LINKAGE, we describe a variety of algorithmic improvements that we have implemented, demonstrating both how these techniques are applied and their power. Experiments show that these improvements speed up the programs by an order of magnitude, on problems of moderate and large size. All improvements were made only in the combinatorial part of the code, without restoring to parallel computers. These improvements synthesize biological principles with computer science techniques, to effectively restructure the time-consuming computations in genetic linkage analysis.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8317490 0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.8317490JDepartment of Cell Biology, Baylor College of Medicine, Houston, TX 77030.~? Couzin, J.2002:Human genome. HapMap launched with pledges of $100 million941-2Science2985595Africa Asia *Chromosome Mapping Genetic Research *Genome, Human *Haplotypes Humans International Cooperation National Institutes of Health (U.S.) Research Support United StatesNov 1fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12411675 1095-9203 (Electronic) News12411675~?Cox, D. G. Kraft, P.2006\Quantification of the power of Hardy-Weinberg equilibrium testing to detect genotyping error10-4 Hum Hered611Alleles *Genotype Haplotypes Heterozygote Humans Linkage Disequilibrium Models, Genetic Models, Statistical Odds Ratio Poisson Distribution Polymorphism, Single Nucleotide Probability Risk)Deviation from Hardy-Weinberg equilibrium has become an accepted test for genotyping error. While it is generally considered that testing departures from Hardy-Weinberg equilibrium to detect genotyping error is not sensitive, little has been done to quantify this sensitivity. Therefore, we have examined various models of genotyping error, including error caused by neighboring SNPs that degrade the performance of genotyping assays. We then calculated the power of chi-square goodness-of-fit tests for deviation from Hardy-Weinberg equilibrium to detect such error. We have also examined the affects of neighboring SNPs on risk estimates in the setting of case-control association studies. We modeled the power of departure from Hardy-Weinberg equilibrium as a test to detect genotyping error and quantified the effect of genotyping error on disease risk estimates. Generally, genotyping error does not generate sufficient deviation from Hardy-Weinberg equilibrium to be detected. As expected, genotyping error due to neighboring SNPs attenuates risk estimates, often drastically. For the moment, the most widely accepted method of detecting genotyping error is to confirm genotypes by sequencing and/or genotyping via a separate method. While these methods are fairly reliable, they are also costly and time consuming.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16514241 g0001-5652 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16514241iDepartment of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. dcox@hsph.harvard.eduC|7Crainiceanu, C. M. Ruppert, D.2004ILikelihood ratio tests in linear mixed models with one variance component165-185IJournal of the Royal Statistical Society Series B-Statistical Methodology66wdegrees of freedom non-regular problems penalized splines nonparametric regression inference parameter boundary splinesWe consider the problem of testing null hypotheses that include restrictions on the variance component in a linear mixed model with one variance component and we derive the finite sample and asymptotic distribution of the likelihood ratio test and the restricted likelihood ratio test. The spectral representations of the likelihood ratio test and the restricted likelihood ratio test statistics are used as the basis of efficient simulation algorithms of their null distributions. The large sample chi(2) mixture approximations using the usual asymptotic theory for a null hypothesis on the boundary of the parameter space have been shown to be poor in simulation studies. Our asymptotic calculations explain these empirical results. The theory of Self and Liang applies only to linear mixed models for which the data vector can be partitioned into a large number of independent and identically distributed subvectors. One-way analysis of variance and penalized splines models illustrate the results.://0001874484000115Part 1 756CN Times Cited:23 Cited References Count:27 1369-7412ISI:000187448400011|Crainiceanu, CM Cornell Univ, Dept Stat, 301 Malott Hall, Ithaca, NY 14853 USA Cornell Univ, Dept Stat, Ithaca, NY 14853 USAEnglish ~?~Crawford, D. C. Carlson, C. S. Rieder, M. J. Carrington, D. P. Yi, Q. Smith, J. D. Eberle, M. A. Kruglyak, L. Nickerson, D. A.2004Haplotype diversity across 100 candidate genes for inflammation, lipid metabolism, and blood pressure regulation in two populations610-22Am J Hum Genet744Africa/ethnology Blood Pressure/*genetics Europe/ethnology Genetic Predisposition to Disease/genetics Haplotypes/*genetics Humans Inflammation/*genetics *Lipid Metabolism Polymorphism, Single Nucleotide/genetics Variation (Genetics)/*geneticsAprRecent studies have suggested that a significant fraction of the human genome is contained in blocks of strong linkage disequilibrium, ranging from ~5 to >100 kb in length, and that within these blocks a few common haplotypes may account for >90% of the observed haplotypes. Furthermore, previous studies have suggested that common haplotypes in candidate genes are generally shared across populations and represent the majority of chromosomes in each population. The conclusions drawn from these preliminary studies, however, are based on an incomplete knowledge of the variation in the regions examined. To bridge this gap in knowledge, we have completely resequenced 100 candidate genes in a population of African descent and one of European descent. Although these genes have been well studied because of their medical importance, we demonstrate that a large amount of sequence variation has not yet been described. We also report that the average number of inferred haplotypes per gene, when complete data is used, is higher than in previous reports and that the number and proportion of all haplotypes represented by common haplotypes per gene is variable. Furthermore, we demonstrate that haplotypes shared between the two populations constitute only a fraction of the total number of haplotypes observed and that these shared haplotypes represent fewer of the African-descent chromosomes than was expected from previous studies. Finally, we show that restricting variation discovery to coding regions does not adequately describe all common haplotypes or the true haplotype block structure observed when all common variation is used to infer haplotypes. These data, derived from complete knowledge of genetic variation in these genes, suggest that the haplotype architecture of candidate genes across the human genome is more complex than previously suggested, with important implications for candidate gene and genomewide association studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15015130 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.15015130kDepartment of Genome Sciences, University of Washington, Seattle 98195-7730, USA. dcrawfo@gs.washington.edu ~?&Cui, J. S. Hopper, J. L. Harrap, S. B.2003VAntihypertensive treatments obscure familial contributions to blood pressure variation207-10 Hypertension412Analysis of Variance Antihypertensive Agents/*therapeutic use Blood Pressure/*drug effects/*genetics/physiology Diastole Family Health Female Humans Male Middle Aged Nuclear Family Phenotype SystoleFebThe linkage and association between inherent blood pressure and underlying genotype is potentially confounded by antihypertensive treatment. We estimated blood pressure variance components (genetic, shared environmental, individual-specific) in 767 adult volunteer families by using a variety of approaches to adjusting blood pressure of the 244 subjects (8.2%) receiving antihypertensive medications. The additive genetic component of variance for systolic pressure was 73.9 mm Hg(2) (SE, 8.8) when measured pressures (adjusted for age by gender within each generation) were used but fell to 61.4 mm Hg(2) (SE, 8.0) when treated subjects were excluded. When the relevant 95th percentile values were substituted for treated systolic pressures, the additive genetic component was 81.9 mm Hg(2) (SE, 9.5), but individual adjustments in systolic pressure ranged from -53.5 mm Hg to +64.5 mm Hg (mean, +17.2 mm Hg). Instead, when 10 mm Hg was added to treated systolic pressure, the additive genetic component rose to 86.6 mm Hg(2) (SE, 10.1). Similar changes were seen in the shared environment component of variance for systolic pressure and for the combined genetic and shared environmental (ie, familial) components of diastolic pressure. There was little change in the individual-specific variance component across any of the methods. Therefore, treated subjects contribute important information to the familial components of blood pressure variance. This information is lost if treated subjects are excluded and obscured by treatment effects if unadjusted measured pressures are used. Adding back an appropriate increment of pressure restores familial components, more closely reflects the pretreatment values, and should increase the power of genomic linkage and linkage disequilibrium analyses.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12574083 G1524-4563 (Electronic) Journal Article Research Support, Non-U.S. Gov't12574083]Centre for Genetic Epidemiology, The University of Melbourne, Parkville, Victoria, Australia.=~?Curtis, D. Sham, P. C.1994FUsing risk calculation to implement an extended relative pair analysis151-62 Ann Hum Genet58Pt 2JAlleles Genetic Markers Humans *Linkage (Genetics) Lod Score Pedigree RiskMayhA new nonparametric method of linkage analysis is described based on identity by descent relationships between all pairs of affected relatives within a pedigree. This approach is an extension of ESPA, which only uses information from pairs of affected siblings. The new method, called ERPA, uses the risk calculation facilities of the LINKAGE programs to obtain the necessary information in a fashion which is simple to implement and which automatically generalizes to allow for marker loci which may be multiple, non-codominant and sex-linked. We have investigated the relative performance of ERPA, ESPA and the lod score method on simulated data. ERPA appears to be more sensitive than ESPA for detecting linkage in pedigrees with small sibships, though both nonparametric methods are inferior to the lod score method when the true mode of transmission can be specified.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7979159 !0003-4800 (Print) Journal Article7979159MAcademic Department of Psychiatry, St Mary's Hospital Medical School, London.e? Dalgaard, P2002Introductory Statistics with R New York, NY.Springer Verlag~?EDaly, M. J. Rioux, J. D. Schaffner, S. F. Hudson, T. J. Lander, E. S.20017High-resolution haplotype structure in the human genome229-32 Nat Genet292Base Sequence Chromosomes, Human, Pair 5 Dna *Genome, Human *Haplotypes Humans Linkage Disequilibrium Markov Chains Molecular Sequence Data Polymorphism, Single NucleotideOctLinkage disequilibrium (LD) analysis is traditionally based on individual genetic markers and often yields an erratic, non-monotonic picture, because the power to detect allelic associations depends on specific properties of each marker, such as frequency and population history. Ideally, LD analysis should be based directly on the underlying haplotype structure of the human genome, but this structure has remained poorly understood. Here we report a high-resolution analysis of the haplotype structure across 500 kilobases on chromosome 5q31 using 103 single-nucleotide polymorphisms (SNPs) in a European-derived population. The results show a picture of discrete haplotype blocks (of tens to hundreds of kilobases), each with limited diversity punctuated by apparent sites of recombination. In addition, we develop an analytical model for LD mapping based on such haplotype blocks. If our observed structure is general (and published data suggest that it may be), it offers a coherent framework for creating a haplotype map of the human genome.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11586305 B1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't11586305Whitehead Institute/Massachusetts Institute of Technology, Center for Genome Research, Cambridge, Massachusetts, USA. mjdaly@genome.wi.mit.edu s|7Dardanoni, V. Forcina, A.1998]A unified approach to likelihood inference on stochastic orderings in a nonparametric context 1112-1123/Journal of the American Statistical Association93443chi-bar-squared distribution likelihood inference likelihood ratio ordering order-restricted inference stochastic ordering uniform ordering inequality constraints linear inequalities survival functions hypothesis tests ratio test distributions populations models regressionSep%For data in a two-way contingency table with ordered margins, we consider various hypotheses of stochastic orders among the conditional distributions considered by rows and show that each is equivalent to requiring that an invertible transformation of the vectors of conditional row probabilities satisfies an appropriate set of linear inequalities. This leads to the construction of a general algorithm for maximum likelihood estimation under multinomial sampling and provides a simple framework for deriving the asymptotic distribution of log-likelihood ratio tests. The usual stochastic ordering and the so called uniform and likelihood ratio orderings are considered as special cases. In particular, for each of these three orderings we determine the transformation required to apply the estimation algorithm: we then consider testing the hypothesis that the rows are identically distributed against the alternative that they are stochastically ordered as well as testing each stochastic order against an unrestricted alternative. We show that in all cases the test statistics are asymptotically distributed as a mixture of chi-squared distributions, with weights determined by the information matrix. By exploiting the special structure of this matrix in these three cases, we find tight upper and lower bounds to the distribution of all test statistics. These bounding distributions rue free of nuisance parameters and relatively easy to compute. Two examples are presented to illustrate the methodology and the required computations needed to apply these techniques.://000076105500036.123BG Times Cited:27 Cited References Count:39 0162-1459ISI:000076105500036Dardanoni, V Univ Palermo, Ist Sci Finanziarie, Fac Econ, I-90132 Palermo, Italy Univ Palermo, Ist Sci Finanziarie, Fac Econ, I-90132 Palermo, Italy Univ Perugia, Fac Econ, Dipartimento Sci Stat, I-06100 Perugia, ItalyEnglish~?Davies, S. Zadik, P. M.1997VComparison of methods for the isolation of methicillin resistant Staphylococcus aureus257-8 J Clin Pathol503Bacteriological Techniques/*standards Ciprofloxacin Culture Media/standards *Methicillin Resistance Sensitivity and Specificity Staphylococcus aureus/*isolation & purification Time FactorsMarThe control of methicillin resistant Staphylococcus aureus (MRSA) relies on the rapid and sensitive detection of carriage. The roles of an enrichment broth, duration of incubation, and Baird-Parker medium containing ciprofloxacin (BPC) were evaluated in comparison with standard media in a centre where the prevalence of ciprofloxacin resistance among MRSA is over 98%. Screening swabs from 402 sites were plated onto BPC, mannitol salt agar (MSA), and MSA with methicillin (MMSA). The swabs were enriched in Tryptone-T broth with 6% salt for 24 hours and the broths subcultured onto BPC, MSA, and MMSA. MRSA was isolated from 134 swabs. Significantly more isolates were obtained by incubating culture plates for 42 hours rather than 18 hours, by the use of broth enrichment, and by addition of methicillin or ciprofloxacin to media. BPC was the most sensitive medium (107 isolates (80%) by direct culture at 42 hours), grew the fewest contaminants, and allowed provisional reporting of 73% of isolates at 18 hours by colonial appearance and use of Staphaurex Plus rapid latex reagent. This may allow the introduction of infection control measures a day earlier than when other established methods are used.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9155681 30021-9746 (Print) Comparative Study Journal Article9155681$Public Health Laboratory, Sheffield. w~?(Davies, W. Isles, A. R. Wilkinson, L. S.2005&Imprinted gene expression in the brain421-30Neurosci Biobehav Rev293Animals Brain/*metabolism Gene Expression/*physiology Gene Expression Regulation, Developmental *Genomic Imprinting Humans Models, GeneticMay"In normal mammals, autosomal genes are present in duplicate (i.e. two alleles), one inherited from the father, and one from the mother. For the majority of genes both alleles are transcribed (or expressed) equally. However, for a small subset of genes, known as imprinted genes, only one allele is expressed in a parent-of-origin dependent manner (note that the 'imprint' here refers to the epigenetic mechanism through which one allele is silenced, and is completely unrelated to classical 'filial imprinting' manifest at the behavioural level). Thus, for some imprinted genes expression is only (or predominantly) seen from the paternally inherited allele, whilst for the remainder, expression is only observed from the maternally inherited allele. Early work on this class of genes highlighted their importance in gross developmental and growth phenotypes. Recent studies in mouse models and humans have emphasised their contribution to brain function and behaviour. In this article, we review the literature concerning the expression of imprinted genes in the brain. In particular, we attempt to define emerging organisation themes, especially in terms of the direction of imprinting (i.e. maternal or paternal expression). We also emphasise the likely role of imprinted genes in neurodevelopment. We end by pointing out that, so far as discerning the precise functions of imprinted genes in the brain is concerned, there are currently more questions than answers; ranging from the extent to which imprinted genes might contribute to common mental disorders, to wider issues related to how easily the new data on brain may be accommodated within the dominant theory regarding the origins and maintenance of imprinting, which pits the maternal and paternal genomes against each other in an evolutionary battle of the sexes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15820547 I0149-7634 (Print) Journal Article Research Support, Non-U.S. Gov't Review15820547lNeurobiology and Developmental Genetics Programmes, The Babraham Institute, Babraham, Cambridge CB2 4AT, UK. ~?)Daw, E. W. Thompson, E. A. Wijsman, E. M.2000EBias in multipoint linkage analysis arising from map misspecification366-80Genet Epidemiol194*Bias (Epidemiology) *Chromosome Mapping Female Humans *Linkage (Genetics) Male Models, Genetic Recombination, Genetic Sex FactorsDecMultipoint linkage analysis methods are often used in human genetic studies. Although multipoint methods increase power for a linkage analysis and will become essential if use of diallelic markers becomes widespread, the methods in use assume an accurate meiotic marker map. Unfortunately, uncertainties in estimates of between-marker meiotic distances are large. Also, sex-averaged maps are generally used, but recombination rates differ in males and females. Both these types of map misspecification can lead to lod score bias, but such bias has not previously been systematically quantified. We examine multipoint lod score bias arising from these map misspecifications, in both the presence and absence of actual linkage. We define bias as the expected difference between the lod score computed under the misspecified map and that computed under the true map. With actual linkage, any map misspecification causes negative bias in lod scores, resulting in loss of power to detect linkage. In most cases, bias is modest, only reaching clearly detectable levels when both types of misspecification are substantial. In the absence of linkage, map misspecification can cause positive or negative bias: falsely assuming a 1:1 female:male ratio always causes positive bias; using too large a distance gives a positive bias; using too small a distance gives a negative bias. This bias can inflate the false-positive rate, especially when the sample size is modest. We conclude that although current sex-averaged maps are suitable for a first-pass multipoint screen, the potential for bias from map misspecification should be evaluated in following up results from such an analysis.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11108646 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11108646Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington 98195-7720, USA. warwick@stat.washington.edu1~?YDawson, E. Abecasis, G. R. Bumpstead, S. Chen, Y. Hunt, S. Beare, D. M. Pabial, J. Dibling, T. Tinsley, E. Kirby, S. Carter, D. Papaspyridonos, M. Livingstone, S. Ganske, R. Lohmussaar, E. Zernant, J. Tonisson, N. Remm, M. Magi, R. Puurand, T. Vilo, J. Kurg, A. Rice, K. Deloukas, P. Mott, R. Metspalu, A. Bentley, D. R. Cardon, L. R. Dunham, I.2002DA first-generation linkage disequilibrium map of human chromosome 22544-8Nature4186897*Chromosome Mapping Chromosomes, Human, Pair 22/*genetics Founder Effect Gene Frequency Haplotypes/genetics Humans Linkage Disequilibrium/*genetics Pedigree Polymorphism, Genetic/genetics Recombination, GeneticAug 1:DNA sequence variants in specific genes or regions of the human genome are responsible for a variety of phenotypes such as disease risk or variable drug response. These variants can be investigated directly, or through their non-random associations with neighbouring markers (called linkage disequilibrium (LD)). Here we report measurement of LD along the complete sequence of human chromosome 22. Duplicate genotyping and analysis of 1,504 markers in Centre d'Etude du Polymorphisme Humain (CEPH) reference families at a median spacing of 15 kilobases (kb) reveals a highly variable pattern of LD along the chromosome, in which extensive regions of nearly complete LD up to 804 kb in length are interspersed with regions of little or no detectable LD. The LD patterns are replicated in a panel of unrelated UK Caucasians. There is a strong correlation between high LD and low recombination frequency in the extant genetic map, suggesting that historical and contemporary recombination rates are similar. This study demonstrates the feasibility of developing genome-wide maps of LD.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12110843 g0028-0836 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.12110843cThe Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.~?de Andrade, M. Amos, C. I.20002Ascertainment issues in variance components models333-44Genet Epidemiol194Family Health Gene Frequency *Genetic Predisposition to Disease Humans *Likelihood Functions *Models, Genetic Quantitative Trait, HeritableDecOne of the main concerns in the family studies of complex diseases is the effect that ascertainment and correction for it may have on test procedures and estimators. Elston and Sobel [1979] and Hopper and Mathews [1982] proposed two ways to correct for ascertainment in the study of quantitative trait data. For single ascertainment, using a variance components approach, we present results of simulation studies comparing estimates from these two methods for different selection criteria. We also show results from simulations when ascertained families are analyzed either at random or by correcting for ascertainment. For discordant sibpairs, we compare a variance components model that incorporates ascertainment correction with the extreme discordant sib pairs (EDSP) design proposed by Risch and Zhang [1995]. Our results show that there is minimal difference between the two methods of ascertainment correction. In the presence of effects from a large genetic background and the segregation of a rare gene, both ascertainment affected the polygenic and environmental components of variance but had rather little impact on the estimate of the linked major gene component of variance. The results also show the EDSP is slightly more powerful the variance components procedures for common alleles, and the variance components procedure is much more powerful than using EDSP when there is a rare allele segregating in the population.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11108643 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11108643{Department of Health Sciences Research, Mayo Clinic and Mayo Foundation, Rochester, Minnesota 55905, USA. mandrade@mayo.edu~?4de Bakker, P. I. Burtt, N. P. Graham, R. R. Guiducci, C. Yelensky, R. Drake, J. A. Bersaglieri, T. Penney, K. L. Butler, J. Young, S. Onofrio, R. C. Lyon, H. N. Stram, D. O. Haiman, C. A. Freedman, M. L. Zhu, X. Cooper, R. Groop, L. Kolonel, L. N. Henderson, B. E. Daly, M. J. Hirschhorn, J. N. Altshuler, D.2006RTransferability of tag SNPs in genetic association studies in multiple populations1298-303 Nat Genet3811VBreast Neoplasms/ethnology/genetics Case-Control Studies Chromosome Mapping/*methods Cohort Studies Computer Simulation Female Genetics, Population/*methods Genome, Human Human Genome Project Humans Linkage Disequilibrium Male *Polymorphism, Single Nucleotide Prostatic Neoplasms/ethnology/genetics *Sequence Tagged Sites Variation (Genetics)NovA general question for linkage disequilibrium-based association studies is how power to detect an association is compromised when tag SNPs are chosen from data in one population sample and then deployed in another sample. Specifically, it is important to know how well tags picked from the HapMap DNA samples capture the variation in other samples. To address this, we collected dense data uniformly across the four HapMap population samples and eleven other population samples. We picked tag SNPs using genotype data we collected in the HapMap samples and then evaluated the effective coverage of these tags in comparison to the entire set of common variants observed in the other samples. We simulated case-control association studies in the non-HapMap samples under a disease model of modest risk, and we observed little loss in power. These results demonstrate that the HapMap DNA samples can be used to select tags for genome-wide association studies in many samples around the world.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17057720 z1061-4036 (Print) Evaluation Studies Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't17057720Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Seven Cambridge Center, Cambridge, Massachusetts, 02142, USA.~?Pde Bakker, P. I. Yelensky, R. Pe'er, I. Gabriel, S. B. Daly, M. J. Altshuler, D.20053Efficiency and power in genetic association studies1217-23 Nat Genet3711 *Algorithms Case-Control Studies Chromosome Mapping Computer Simulation Genetic Markers/genetics *Genetic Predisposition to Disease Haplotypes/*genetics Humans Linkage Disequilibrium/genetics Models, Genetic Polymorphism, Single Nucleotide/*genetics Reference StandardsNovgWe investigated selection and analysis of tag SNPs for genome-wide association studies by specifically examining the relationship between investment in genotyping and statistical power. Do pairwise or multimarker methods maximize efficiency and power? To what extent is power compromised when tags are selected from an incomplete resource such as HapMap? We addressed these questions using genotype data from the HapMap ENCODE project, association studies simulated under a realistic disease model, and empirical correction for multiple hypothesis testing. We demonstrate a haplotype-based tagging method that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency. Examining all observed haplotypes for association, rather than just those that are proxies for known SNPs, increases power to detect rare causal alleles, at the cost of reduced power to detect common causal alleles. Power is robust to the completeness of the reference panel from which tags are selected. These findings have implications for prioritizing tag SNPs and interpreting association studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16244653 g1061-4036 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16244653Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, CPZN-6818, Boston, Massachusetts 02114-2790, USA. ~?9de Geus, E. J. Wright, M. J. Martin, N. G. Boomsma, D. I.2001(Genetics of brain function and cognition489-95 Behav Genet316uBrain/*physiology Humans Intelligence/*genetics *Phenotype Quantitative Trait, Heritable Twin Studies Twins/*geneticsNovThere is overwhelming evidence for the existence of substantial genetic influences on individual differences in general and specific cognitive abilities, especially in adults. The actual localization and identification of genes underlying variation in cognitive abilities and intelligence has only just started, however. Successes are currently limited to neurological mutations with rather severe cognitive effects. The current approaches to trace genes responsible for variation in the normal ranges of cognitive ability consist of large scale linkage and association studies. These are hampered by the usual problems of low statistical power to detect quantitative trait loci (QTLs) of small effect. One strategy to boost the power of genomic searches is to employ endophenotypes of cognition derived from the booming field of cognitive neuroscience. This special issue of Behavior Genetics reports on one of the first genome-wide association studies for general IQ. A second paper summarizes candidate genes for cognition, based on animal studies. A series of papers then introduces two additional levels of analysis in the "black box" between genes and cognitive ability: (1) behavioral measures of information-processing speed (inspection time, reaction time, rapid naming) and working memory capacity (performance on on single or dual tasks of verbal and spatio-visual working memory), and (2) electrophyiosological derived measures of brain function (e.g., event-related potentials). The obvious way to assess the reliability and validity of these endophenotypes and their usefulness in the search for cognitive ability genes is through the examination of their genetic architecture in twin family studies. Papers in this special issue show that much of the association between intelligence and speed-of-information processing/brain function is due to a common gene or set of genes, and thereby demonstrate the usefulness of considering these measures in gene-hunting studies for IQ.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11838528 D0001-8244 (Print) Comment Editorial Research Support, Non-U.S. Gov't118385283~?de la Chapelle, A.1993JDisease gene mapping in isolated human populations: the example of Finland857-65 J Med Genet3010Finland/epidemiology Gene Frequency Genes, Dominant Genes, Recessive Genetic Diseases, Inborn/*epidemiology *Genetics, Population Humans Linkage Disequilibrium Mutation X ChromosomeOctehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8230163 (0022-2593 (Print) Journal Article Review8230163@Department of Medical Genetics, University of Helsinki, Finland. 2~? de la Chapelle, A. Wright, F. A.1998XLinkage disequilibrium mapping in isolated populations: the example of Finland revisited12416-23Proc Natl Acad Sci U S A9521sFinland Founder Effect Genetic Diseases, Inborn/*genetics Genetic Markers Haplotypes Humans *Linkage DisequilibriumOct 13lLinkage disequilibrium analysis can provide high resolution in the mapping of disease genes because it incorporates information on recombinations that have occurred during the entire period from the mutational event to the present. A circumstance particularly favorable for high-resolution mapping is when a single founding mutation segregates in an isolated population. We review here the population structure of Finland in which a small founder population some 100 generations ago has expanded into 5.1 million people today. Among the 30-odd autosomal recessive disorders that are more prevalent in Finland than elsewhere, several appear to have segregated for this entire period in the "panmictic" southern Finnish population. Linkage disequilibrium analysis has allowed precise mapping and determination of genetic distances at the 0.1-cM level in several of these disorders. Estimates of genetic distance have proven accurate, but previous calculations of the confidence intervals were too small because sampling variation was ignored. In the north and east of Finland the population can be viewed as having been "founded" only after 1500. Disease mutations that have undergone such a founding bottleneck only 20 or so generations ago exhibit linkage disequilibrium and haplotype sharing over long genetic distances (5-15 cM). These features have been successfully exploited in the mapping and cloning of many genes. We review the statistical issues of fine mapping by linkage disequilibrium and suggest that improved methodologies may be necessary to map diseases of complex etiology that may have arisen from multiple founding mutations.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9770501 g0027-8424 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.9770501Human Cancer Genetics Program, Comprehensive Cancer Center, Ohio State University, 420 West 12th Avenue, Columbus, OH 43210-1214, USA. delachapellel-1@medctr.osu.edu~?DeFries, J. C. Fulker, D. W.1986=Multivariate behavioral genetics and development: an overview1-10 Behav Genet161mFemale *Genetics, Behavioral/history History, 20th Century *Human Development Humans Individuality StatisticsJanehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3518694 z0001-8244 (Print) Historical Article Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.3518694 ~?DeLisi, L. E. Shaw, S. H. Crow, T. J. Shields, G. Smith, A. B. Larach, V. W. Wellman, N. Loftus, J. Nanthakumar, B. Razi, K. Stewart, J. Comazzi, M. Vita, A. Heffner, T. Sherrington, R.2002yA genome-wide scan for linkage to chromosomal regions in 382 sibling pairs with schizophrenia or schizoaffective disorder803-12Am J Psychiatry1595uChromosome Mapping/methods/*statistics & numerical data Chromosomes, Human/genetics Family Female Gene Expression/genetics Gene Frequency Genetic Markers Genome, Human Genomic Imprinting/genetics Genotype Humans Lod Score Male Microsatellite Repeats Pedigree Polymorphism, Genetic Psychotic Disorders/*genetics Schizophrenia/*genetics Sequence Analysis Variation (Genetics)MayOBJECTIVE: Some genome-wide scans and association studies for schizophrenia susceptibility genes have yielded significant positive findings, but there is disagreement between studies on their locations, and no mutation has yet been found in any gene. Since schizophrenia is a complex disorder, a study with sufficient power to detect a locus with a small or moderate gene effect is necessary. METHOD: In a genome-wide scan of 382 sibling pairs with a diagnosis of schizophrenia or schizoaffective disorder, 396 highly polymorphic markers spaced approximately 10 centimorgans apart throughout the genome were genotyped in all individuals. Multipoint nonparametric linkage analysis was performed to evaluate regions of the genome demonstrating increased allele sharing, as measured by a lod score. RESULTS: Two regions with multipoint maximum lod scores suggesting linkage were found. The highest lod scores occurred on chromosome 10p15-p13 (peak lod score of 3.60 at marker D10S189) and the centromeric region of chromosome 2 (peak lod score of 2.99 at marker D2S139). In addition, a maximum lod score of 2.00 was observed with marker D22S283 on chromosome 22q12, which showed evidence of an imprinting effect, whereby an excess sharing of maternal, but not paternal, alleles was present. No evidence of linkage was obtained at several locations identified in previous studies, including chromosomes 1q, 4p, 5p-q, 6p, 8p, 13q, 15p, and 18p. CONCLUSIONS: The findings of this large genome-wide scan emphasize the weakness and fragility of linkage reports on schizophrenia. No linkage appears to be consistently replicable across large studies. Thus, it has to be questioned whether the genetic contribution to this disorder is detectable by these strategies and the possibility raised that it may be epigenetic, i.e., related to gene expression rather than sequence variation. Nevertheless, the positive findings on chromosome 2, 10, and 22 should be pursued further.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11986135 y0002-953X (Print) Journal Article Multicenter Study Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11986135XDepartment of Psychiatry, New York University, New York, NY 10016, USA. DeLisi76@aol.com|7)Dempster, A. P. Laird, N. M. Rubin, D. B.19778Maximum Likelihood from Incomplete Data Via Em Algorithm1-38@Journal of the Royal Statistical Society Series B-Methodological391://A1977DM464000010Dm464 Times Cited:6627 Cited References Count:41 0035-9246ISI:A1977DM46400001DHarvard Univ,Cambridge,Ma 02138 Educ Testing Serv,Princeton,Nj 08540English~?Deng, H. W. Chen, W. M.2001fThe power of the transmission disequilibrium test (TDT) with both case-parent and control-parent trios289-302 Genet Res783Case-Control Studies Computational Biology/*methods *Computer Simulation Diabetes Mellitus, Type 1/genetics Genetic Predisposition to Disease Humans Insulin/genetics *Linkage Disequilibrium Models, Genetic *Multifactorial InheritanceDecThe transmission disequilibrium test (TDT) customarily uses affected children and their parents (often case-parent trios, TDTD). Control-parent trios are necessary to guard against spurious significant results due to segregation distortion but are not generally utilized in the identification of disease susceptibility loci (DSL). Controls are often easy to recruit and the TDT can easily be extended to include control-parent trios into the analyses with unrelated case-parent trios. We present an extension of the TDT (TDTDC) that incorporates unrelated cases and controls and their parents into a single analysis. We develop a simple and accurate analytical method for computing the statistical power of various TDT (e.g. the TDTD, TDTDC, TDTDC and TDTC that employ control-parent trios only) under any genetic model. We investigated the power of these TDT, and particularly compared the relative power of the TDTD and TDTDC. We found that the TDTDC is almost always more powerful than the TDTC and TDTD. The relative power of the TDTDC and TDTD depends largely upon a number of parameters identified in the study. This study provides a basis for efficient use of control-parent trios in DSL identification.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11865718 0016-6723 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Validation Studies11865718Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, ChangSha, Hunan 410081, P. R. China. deng@creighton.edu~?Denison, D. G. Holmes, C. C.20011Bayesian partitioning for estimating disease risk143-9 Biometrics571*Bayes Theorem Biometry Cluster Analysis Data Interpretation, Statistical Disease/*etiology Hazardous Waste/adverse effects Humans Leukemia/epidemiology/etiology Markov Chains Monte Carlo Method New York/epidemiology Nonlinear Dynamics Probability *RiskMarThis paper presents a Bayesian nonlinear approach for the analysis of spatial count data. It extends the Bayesian partition methodology of Holmes, Denison, and Mallick (1999, Bayesian partitioning for classification and regression, Technical Report, Imperial College, London) to handle data that involve counts. A demonstration involving incidence rates of leukemia in New York state is used to highlight the methodology. The model allows us to make probability statements on the incidence rates around point sources without making any parametric assumptions about the nature of the influence between the sources and the surrounding location.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11252589 B0006-341X (Print) Journal Article Research Support, Non-U.S. Gov't11252589KDepartment of Mathematics, Imperial College, London, UK. d.denison@ic.ac.uk~?(Derks, E. M. Dolan, C. V. Boomsma, D. I.2004IEffects of censoring on parameter estimates and power in genetic modeling659-69Twin Res76Computer Simulation Humans *Models, Genetic Multivariate Analysis Sleep Disorders/genetics Twin Studies/*statistics & numerical data Twins/*geneticsDec)Genetic and environmental influences on variance in phenotypic traits may be estimated with normal theory Maximum Likelihood (ML). However, when the assumption of multivariate normality is not met, this method may result in biased parameter estimates and incorrect likelihood ratio tests. We simulated multivariate normal distributed twin data under the assumption of three different genetic models. Genetic model fitting was performed in six data sets: multivariate normal data, discrete uncensored data, censored data, square root transformed censored data, normal scores of censored data, and categorical data. Estimates were obtained with normal theory ML (data sets 1-5) and with categorical data analysis (data set 6). Statistical power was examined by fitting reduced models to the data. When fitting an ACE model to censored data, an unbiased estimate of the additive genetic effect was obtained. However, the common environmental effect was underestimated and the unique environmental effect was overestimated. Transformations did not remove this bias. When fitting an ADE model, the additive genetic effect was underestimated while the dominant and unique environmental effects were overestimated. In all models, the correct parameter estimates were recovered with categorical data analysis. However, with categorical data analysis, the statistical power decreased. The analysis of L-shaped distributed data with normal theory ML results in biased parameter estimates. Unbiased parameter estimates are obtained with categorical data analysis, but the power decreases.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15607017 g1369-0523 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15607017gDepartment of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands. em.derks@psy.vu.nl 8~?(Derks, E. M. Dolan, C. V. Boomsma, D. I.2007jStatistical power to detect genetic and environmental influences in the presence of data missing at random159-67Twin Res Hum Genet101Analysis of Variance *Computer Simulation Female Humans Male *Models, Genetic *Models, Statistical Quantitative Trait, Heritable Sample Size *Software Twin Studies Twins, Dizygotic/*genetics Twins, Monozygotic/*geneticsFeb8We study the situation in which a cheap measure (X) is observed in a large, representative twin sample, and a more expensive measure (Y) is observed in a selected subsample. The aim of this study is to investigate the optimal selection design in terms of the statistical power to detect genetic and environmental influences on the variance of Y and on the covariance of X and Y. Data were simulated for 4000 dizygotic and 2000 monozygotic twins. Missingness (87% vs. 97%) was then introduced in accordance with 7 selection designs: (i) concordant low + individual high design; (ii) extreme concordant design; (iii) extreme concordant and discordant design (EDAC); (iv) extreme discordant design; (v) individual score selection design; (vi) selection of an optimal number of MZ and DZ twins; and (vii) missing completely at random. The statistical power to detect the influence of additive and dominant genetic and shared environmental effects on the variance of Y and on the covariance between X and Y was investigated. The best selection design is the individual score selection design. The power to detect additive genetic effects is high irrespective of the percentage of missingness or selection design. The power to detect shared environmental effects is acceptable when the percentage of missingness is 87%, but is low when the percentage of missingness is 97%, except for the individual score selection design, in which the power remains acceptable. The power to detect D is low, irrespective of selection design or percentage of missingness. The individual score selection design is therefore the best design for detecting genetic and environmental influences on the variance of Y and on the covariance of X and Y. However, the EDAC design may be preferred when an additional purpose of a study is to detect quantitative trait loci effects.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17539375 g1832-4274 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't17539375gDepartment of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands. em.derks@psy.vu.nl~?#Devlin, B. Bacanu, S. A. Roeder, K.2004Genomic Control to the extreme1129-30; author reply 1131 Nat Genet3611c*Data Interpretation, Statistical Epidemiologic Measurements *Genetics, Population *Genotype HumansNovfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15514657 1061-4036 (Print) Comment Letter15514657 ~?!Devlin, B. Daniels, M. Roeder, K.1997The heritability of IQ468-71Nature3886641Adolescent Adult Aging/genetics/physiology Child Embryonic and Fetal Development/genetics/physiology Environment Humans Intelligence/*genetics/physiology Models, Genetic Models, Statistical Twins/geneticsJul 31IQ heritability, the portion of a population's IQ variability attributable to the effects of genes, has been investigated for nearly a century, yet it remains controversial. Covariance between relatives may be due not only to genes, but also to shared environments, and most previous models have assumed different degrees of similarity induced by environments specific to twins, to non-twin siblings (henceforth siblings), and to parents and offspring. We now evaluate an alternative model that replaces these three environments by two maternal womb environments, one for twins and another for siblings, along with a common home environment. Meta-analysis of 212 previous studies shows that our 'maternal-effects' model fits the data better than the 'family-environments' model. Maternal effects, often assumed to be negligible, account for 20% of covariance between twins and 5% between siblings, and the effects of genes are correspondingly reduced, with two measures of heritability being less than 50%. The shared maternal environment may explain the striking correlation between the IQs of twins, especially those of adult twins that were reared apart. IQ heritability increases during early childhood, but whether it stabilizes thereafter remains unclear. A recent study of octogenarians, for instance, suggests that IQ heritability either remains constant through adolescence and adulthood, or continues to increase with age. Although the latter hypothesis has recently been endorsed, it gathers only modest statistical support in our analysis when compared to the maternal-effects hypothesis. Our analysis suggests that it will be important to understand the basis for these maternal effects if ways in which IQ might be increased are to be identified.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9242404 z0028-0836 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Twin Study9242404uDepartment of Psychiatry, University of Pittsburgh School of Medicine, Pennsylvania 15213, USA. devlinbj@msx.upmc.edu~?Devlin, B. Risch, N.1995FA comparison of linkage disequilibrium measures for fine-scale mapping311-22Genomics292Alleles Case-Control Studies *Chromosome Mapping Evolution Genetic Diseases, Inborn/*genetics Genetic Markers Haplotypes Humans *Linkage Disequilibrium Models, Genetic Models, Statistical Sensitivity and Specificity Stochastic ProcessesSep 20Linkage mapping generally localizes disease genes to 1- to 2-cM regions of chromosomes. In theory, further refinement of location can be achieved by population-based studies of linkage disequilibrium between disease locus alleles and alleles at adjacent markers. One approach to localization, dubbed simple disequilibrium mapping, is to determine the relative location of the disease locus by plotting disequilibrium values against marker locations. We investigate the simple mapping properties of five disequilibrium measures, the correlation coefficient delta, Lewontin's D', the robust formulation of the population attributable risk delta, Yule's Q, and Kaplan and Weir's proportional difference d under the assumption of initial complete disequilibrium between disease and marker loci. The studies indicate that delta is a superior measure for fine mapping because it is directly related to the recombination fraction between the disease and the marker loci, and it is invariant when disease haplotypes are sampled at a rate higher than their population frequencies, as in a case-control study. D' yields results comparable to those of delta in many realistic settings. Of the remaining three measures, Q, delta, and d, Q yields the best results. From simulations of short-term evolution, all measures show some sensitivity to marker allele frequencies; however, as predicted by analytic results, Q, delta, and d exhibit the greatest sensitivity to variation in marker allele frequencies across loci.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8666377 X0888-7543 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.8666377nDepartment of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut, USA.8~?Devlin, B. Roeder, K.1999'Genomic control for association studies997-1004 Biometrics554yBayes Theorem *Biometry Case-Control Studies Genetic Techniques *Genome, Human Humans Models, Genetic Models, StatisticalDecA dense set of single nucleotide polymorphisms (SNP) covering the genome and an efficient method to assess SNP genotypes are expected to be available in the near future. An outstanding question is how to use these technologies efficiently to identify genes affecting liability to complex disorders. To achieve this goal, we propose a statistical method that has several optimal properties: It can be used with case control data and yet, like family-based designs, controls for population heterogeneity; it is insensitive to the usual violations of model assumptions, such as cases failing to be strictly independent; and, by using Bayesian outlier methods, it circumvents the need for Bonferroni correction for multiple tests, leading to better performance in many settings while still constraining risk for false positives. The performance of our genomic control method is quite good for plausible effects of liability genes, which bodes well for future genetic analyses of complex disorders.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11315092 0006-341X (Print) Comparative Study Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.11315092bDepartment of Psychiatry, University of Pittsburgh, Pennsylvania 15213, USA. devlinbj@msx.upmc.edu ~?#Devlin, B. Roeder, K. Wasserman, L.2001DGenomic control, a new approach to genetic-based association studies155-66Theor Popul Biol603Alleles Bias (Epidemiology) Case-Control Studies Chi-Square Distribution Confounding Factors (Epidemiology) Genetic Heterogeneity *Genetic Markers *Genetics, Population Genotype Humans *Models, Genetic Models, TheoreticalNovDuring the past decade, mutations affecting liability to human disease have been discovered at a phenomenal rate, and that rate is increasing. For the most part, however, those diseases have a relatively simple genetic basis. For diseases with a complex genetic and environmental basis, new approaches are needed to pave the way for more rapid discovery of genes affecting liability. One such approach exploits large, population-based samples and large-scale genotyping to evaluate disease/gene associations. A substantial drawback to such samples is the fact that population heterogeneity can induce spurious associations between genes and disease. We describe a method called genomic control (GC), which obviates many of the concerns about population substructure by using the features of the genomes present in the sample to correct for stratification. Two such approaches are now available. The GC approach exploits the fact that population substructure generate "overdispersion" of statistics used to assess association. By testing multiple polymorphisms throughout the genome, only some of which are pertinent to the disease of interest, the degree of overdispersion generated by population substructure can be estimated and taken into account. The other approach, called Structured Association (SA), assumes that the sampled population, while heterogeneous, is composed of subpopulations that are themselves homogeneous. By using multiple polymorphisms throughout the genome, SA probabilistically assigns sampled individuals to these latent subpopulations. We review in detail the overdispersion GC. In addition to outlining the published ideas on this method, we describe several extensions: quantitative trait studies and case-control studies with haplotypes and multiallelic markers. For each study design our goal is to achieve control similar to that obtained for a family-based study, but with the convenience found in a population-based design.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11855950 v0040-5809 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Review11855950nDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA. devlinbj@msx.upmc.edu o~?Diao, G. Lin, D. Y.2006RImproving the power of association tests for quantitative traits in family studies301-13Genet Epidemiol304Alcoholism/genetics Computer Simulation Family Health Humans Likelihood Functions Linkage Disequilibrium/*genetics *Models, Genetic *Models, Statistical *Quantitative Trait, HeritableMayAssociation mapping based on family studies can identify genes that influence complex human traits while providing protection against population stratification. Because no gene is likely to have a very large effect on a complex trait, most family studies have limited power. Among the commonly used family-based tests of association for quantitative traits, the quantitative transmission-disequilibrium tests (QTDT) based on the variance-components model is the most flexible and most powerful. This method assumes that the trait values are normally distributed. Departures from normality can inflate the type I error and reduce the power. Although the family-based association tests (FBAT) and pedigree disequilibrium tests (PDT) do not require normal traits, nonnormality can also result in loss of power. In many cases, approximate normality can be achieved by transforming the trait values. However, the true transformation is unknown, and incorrect transformations may compromise the type I error and power. We propose a novel class of association tests for arbitrarily distributed quantitative traits by allowing the true transformation function to be completely unspecified and empirically estimated from the data. Extensive simulation studies showed that the new methods provide accurate control of the type I error and can be substantially more powerful than the existing methods. We applied the new methods to the Collaborative Study on the Genetics of Alcoholism and discovered significant association of single nucleotide polymorphisms (SNP) tsc0022400 on chromosome 7 with the quantitative electrophysiological phenotype TTTH1, which was not detected by any existing methods. We have implemented the new methods in a freely available computer program.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16607624 F0741-0395 (Print) Journal Article Research Support, N.I.H., Extramural16607624gDepartment of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599-7420, USA.+~?=Diego, V. P. Almasy, L. Dyer, T. D. Soler, J. M. Blangero, J.2003Strategy and model building in the fourth dimension: a null model for genotype x age interaction as a Gaussian stationary stochastic processS34 BMC Genet 4 Suppl 1Adult Adult Children Aged Aging/*genetics/physiology Blood Glucose/genetics/physiology Blood Pressure/genetics Cardiovascular System/chemistry/metabolism Cohort Studies Fasting/blood Female Genotype Humans Linkage (Genetics)/genetics Male Middle Aged *Models, Statistical Multifactorial Inheritance/genetics Normal Distribution Quantitative Trait Loci/genetics Quantitative Trait, Heritable Stochastic Processes SystoleIBACKGROUND: Using univariate and multivariate variance components linkage analysis methods, we studied possible genotype x age interaction in cardiovascular phenotypes related to the aging process from the Framingham Heart Study. RESULTS: We found evidence for genotype x age interaction for fasting glucose and systolic blood pressure. CONCLUSIONS: There is polygenic genotype x age interaction for fasting glucose and systolic blood pressure and quantitative trait locus x age interaction for a linkage signal for systolic blood pressure phenotypes located on chromosome 17 at 67 cM.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14975102 K1471-2156 (Electronic) Journal Article Research Support, U.S. Gov't, P.H.S.14975102uDepartment of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas, USA. vdiego@darwin.sfbr.org~?Ding, K. Kullo, I. J.2007]Methods for the selection of tagging SNPs: a comparison of tagging efficiency and performance228-36Eur J Hum Genet152Chromosome Mapping/*methods European Continental Ancestry Group/genetics *Genetic Predisposition to Disease Haplotypes Humans *Linkage Disequilibrium *Polymorphism, Single Nucleotide Reproducibility of Results Sensitivity and Specificity *Sequence Tagged SitesFeb(There is great interest in the use of tagging single nucleotide polymorphisms (tSNPs) to facilitate association studies of complex diseases. This is based on the premise that a minimum set of tSNPs may be sufficient to capture most of the variation in certain regions of the human genome. Several methods have been described to select tSNPs, based on either haplotype-block structure or independent of the underlying block structure. In this paper, we compare eight methods for choosing tSNPs in 10 representative resequenced candidate genes (a total of 194.2 kb) with different levels of linkage disequilibrium (LD) in a sample of European-Americans. We compared tagging efficiency (TE) and prediction accuracy of tSNPs identified by these methods, as a function of several factors, including LD level, minor allele frequency, and tagging criteria. We also assessed tagging consistency between each method. We found that tSNPs selected based on the methods Haplotype Diversity and Haplotype r2 provided the highest TE, whereas the prediction accuracy was comparable among different methods. Tagging consistency between different methods of tSNPs selection was poor. This work demonstrates that when tSNPs-based association studies are undertaken, the choice of method for selecting tSNPs requires careful consideration.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17164795 X1018-4813 (Print) Comparative Study Journal Article Research Support, N.I.H., Extramural17164795ZDivision of Cardiovascular Diseases, Mayo Clinic and Foundation, Rochester, MN 55905, USA.|7'Dolan, C. van der Sluis, S. Grasman, R.2005WA note on normal theory power calculation in SEM with data missing completely at random245-2628Structural Equation Modeling-a Multidisciplinary Journal1222structural equation models monte-carlo sample-sizeWe consider power calculation in structural equation modeling with data missing completely at random (MCAR). Muthen and Muthen (2002) recently demonstrated how power calculations with data MCAR can be carried out by means of a Monte Carlo study. Here we show that the method of Satorra and Saris (1985), which is based on the nonnull distribution of the (normal theory) log-likelihood ratio test, can also be used. Compared to a Monte Carlo study, this method is computationally less intensive. We discuss 2 ways to calculate power when data are MCAR, one based on multigroup analysis and summary statistics, the other based on transformed raw data. The latter method is quite simple to carry out. Four examples are presented. This article is limited to data MCAR. Generally MCAR is a strong assumption. We demonstrate that results of power analyses based on the MCAR assumption are not informative if the data are actually missing at random.://000228626500004-919OD Times Cited:3 Cited References Count:31 1070-5511ISI:000228626500004Dolan, C Univ Amsterdam, Dept Psychol, Roetersstr 15, NL-1018 WB Amsterdam, Netherlands Univ Amsterdam, Dept Psychol, NL-1018 WB Amsterdam, NetherlandsEnglish~?Dolan, C. V. Boomsma, D. I.1998dOptimal selection of sib pairs from random samples for linkage analysis of a QTL using the EDAC test197-206 Behav Genet283Alleles *Computer Simulation *Genetic Markers Humans Linkage (Genetics)/*genetics Models, Genetic *Nuclear Family Patient Selection Phenotype Probability *Quantitative Trait, Heritable Research Design/*standards Sample Size Sampling StudiesMay8Percentages of extremely concordant and extremely discordant sib pairs are calculated that maximize the power to detect a quantitative trait locus (QTL) under a variety of circumstances using the EDAC test. We assume a large fixed number of randomly sampled sib pairs, such as one would hope to find in the large twin registries, and limited resources to genotype a certain number of selected sib pairs. Our aim is to investigate whether optimal selection can be achieved when prior knowledge concerning the QTL gene action, QTL allele frequency, QTL effect size, and background (residual) sib correlation is limited or absent. To this end we calculate the best selection percentages for a large number of models, which differ in QTL gene action allele frequency, background correlation, and QTL effect size. By averaging these percentages over gene action, over allele frequency, over gene action, and over allele frequencies, we arrive at general recommendations concerning selection percentages. The soundness of these recommendations is subsequently in a number of test cases.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9670595 !0001-8244 (Print) Journal Article9670595eDepartment of Psychology, Vrije Universiteit, Amsterdam, The Netherlands. op_dolan@macmail.psy.uva.nlA~?(Dolan, C. V. Boomsma, D. I. Neale, M. C.1999pA note on the power provided by sibships of sizes 2, 3, and 4 in genetic covariance modeling of a codominant QTL163-70 Behav Genet293k*Alleles Analysis of Variance *Family Characteristics Humans *Models, Genetic *Phenotype Social EnvironmentMayThe contribution of size 3 and size 4 sibships to power in covariance structure modeling of a codominant QTL is investigated. Power calculations are based on the noncentral chi-square distribution. Sixteen sets of parameter values are considered. Results indicate that size 3 and size 4 sibships provided large increases in power over size 2 sibships. On average a size 3 (4) sibship is 3 (6 to 7) times as informative as a size 2 sibship. The increase in power does not depend on the specific effects sizes of the independent variables in the model. These findings extend results presented by Fulker and Cherny (1996) and Schork (1993). We consider the informativeness of the size 2, 3, and 4 sibships, which differ in the unique configuration of IBD sharing. Three of the 10 size 3 and 7 of the 36 size 4 sibships are particularly informative. The results presented concern random (unselective) sampling but do have implications for selective sampling.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10547922 g0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.10547922_Department of Psychology, University of Amsterdam, The Netherlands. op_dolan@macmail.psy.uva.nl~?(Dolan, C. V. Boomsma, D. I. Neale, M. C.1999A simulation study of the effects of assignment of prior identity-by-descent probabilities to unselected sib pairs, in covariance-structure modeling of a quantitative-trait locus268-80Am J Hum Genet641uAlleles Gene Frequency Genetic Markers Humans *Models, Genetic Nuclear Family *Quantitative Trait, Heritable SoftwareJan]Sib pair-selection strategies, designed to identify the most informative sib pairs in order to detect a quantitative-trait locus (QTL), give rise to a missing-data problem in genetic covariance-structure modeling of QTL effects. After selection, phenotypic data are available for all sibs, but marker data-and, consequently, the identity-by-descent (IBD) probabilities-are available only in selected sib pairs. One possible solution to this missing-data problem is to assign prior IBD probabilities (i.e., expected values) to the unselected sib pairs. The effect of this assignment in genetic covariance-structure modeling is investigated in the present paper. Two maximum-likelihood approaches to estimation are considered, the pi-hat approach and the IBD-mixture approach. In the simulations, sample size, selection criteria, QTL-increaser allele frequency, and gene action are manipulated. The results indicate that the assignment of prior IBD probabilities results in serious estimation bias in the pi-hat approach. Bias is also present in the IBD-mixture approach, although here the bias is generally much smaller. The null distribution of the log-likelihood ratio (i.e., in absence of any QTL effect) does not follow the expected null distribution in the pi-hat approach after selection. In the IBD-mixture approach, the null distribution does agree with expectation.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9915966 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't9915966YPsychology Faculty, University of Amsterdam, The Netherlands. op_dolan@macmail.psy.uva.nl~?GDominicus, A. Skrondal, A. Gjessing, H. K. Pedersen, N. L. Palmgren, J.2006ELikelihood ratio tests in behavioral genetics: problems and solutions331-40 Behav Genet362Analysis of Variance Bayes Theorem Chi-Square Distribution Genetics, Behavioral/*statistics & numerical data Genotype Humans *Likelihood Functions Models, Genetic Probability Social Environment Twin StudiesMarThe likelihood ratio test of nested models for family data plays an important role in the assessment of genetic and environmental influences on the variation in traits. The test is routinely based on the assumption that the test statistic follows a chi-square distribution under the null, with the number of restricted parameters as degrees of freedom. However, tests of variance components constrained to be non-negative correspond to tests of parameters on the boundary of the parameter space. In this situation the standard test procedure provides too large p-values and the use of the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) for model selection is problematic. Focusing on the classical ACE twin model for univariate traits, we adapt existing theory to show that the asymptotic distribution for the likelihood ratio statistic is a mixture of chi-square distributions, and we derive the mixing probabilities. We conclude that when testing the AE or the CE model against the ACE model, the p-values obtained from using the chi(2)(1 df) as the reference distribution should be halved. When the E model is tested against the ACE model, a mixture of chi(2)(0 df), chi(2)(1 df) and chi(2)(2 df) should be used as the reference distribution, and we provide a simple formula to compute the mixing probabilities. Similar results for tests of the AE, DE and E models against the ADE model are also derived. Failing to use the appropriate reference distribution can lead to invalid conclusions.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16474914 g0001-8244 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16474914VDepartment of Mathematics, Stockholm University, Stockholm, Sweden. annicad@math.su.se,~?rDong, C. Li, W. D. Geller, F. Lei, L. Li, D. Gorlova, O. Y. Hebebrand, J. Amos, C. I. Nicholls, R. D. Price, R. A.2005GPossible genomic imprinting of three human obesity-related genetic loci427-37Am J Hum Genet7632Adipose Tissue/pathology Body Mass Index Chromosomes, Human, Pair 10/genetics Chromosomes, Human, Pair 12/genetics Chromosomes, Human, Pair 13/genetics Female Genetic Markers *Genomic Imprinting Genomics Humans Linkage (Genetics) Lod Score Male Obesity/*genetics/pathology Phenotype Quantitative Trait LociMarTo detect potentially imprinted, obesity-related genetic loci, we performed genomewide parent-of-origin linkage analyses under an allele-sharing model for discrete traits and under a family regression model for obesity-related quantitative traits, using a European American sample of 1,297 individuals from 260 families, with 391 microsatellite markers. We also used two smaller, independent samples for replication (a sample of 370 German individuals from 89 families and a sample of 277 African American individuals from 52 families). For discrete-trait analysis, we found evidence for a maternal effect in chromosome region 10p12 across the three samples, with LOD scores of 5.69 (single-point) and 4.52 (multipoint) for the pooled sample. For quantitative-trait analysis, we found the strongest evidence for a maternal effect (single-point LOD of 2.85; multipoint LOD of 4.01 for body mass index [BMI] and 3.69 for waist circumference) in region 12q24 and for a paternal effect (single-point LOD of 4.79; multipoint LOD of 3.72 for BMI) in region 13q32, in the European American sample. The results suggest that parent-of-origin effects, perhaps including genomic imprinting, may play a role in human obesity.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15647995 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.15647995}Center for Neurobiology and Behavior, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-6140, USA.~?DDouglas, J. A. Boehnke, M. Gillanders, E. Trent, J. M. Gruber, S. B.2001iExperimentally-derived haplotypes substantially increase the efficiency of linkage disequilibrium studies361-4 Nat Genet284Animals Chromosomes, Human/genetics Female Gene Frequency Genetic Markers Genotype Haplotypes/*genetics Humans Hybrid Cells/cytology/*physiology In Situ Hybridization, Fluorescence Linkage Disequilibrium/*genetics Male Mice Polymerase Chain ReactionAugThe study of complex genetic traits in humans is limited by the expense and difficulty of ascertaining populations of sufficient sample size to detect subtle genetic contributions to disease. Here we introduce an application of a somatic cell hybrid construction strategy called conversion that maximizes the genotypic information from each sampled individual. The approach permits direct observation of individual haplotypes, thereby eliminating the need for collecting and genotyping DNA from family members for haplotype-based analyses. We describe experimental data that validate the use of conversion as a whole-genome haplotyping tool and evaluate the theoretical efficiency of using conversion-derived haplotypes instead of conventional genotypes in the context of haplotype-frequency estimation. We show that, particularly when phenotyping is expensive, conversion-based haplotyping can be more efficient and cost-effective than standard genotyping.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11443299 g1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11443299ODepartment of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA. S~?$Douglas, J. A. Boehnke, M. Lange, K.2000^A multipoint method for detecting genotyping errors and mutations in sibling-pair linkage data1287-97Am J Hum Genet664vAlleles Chromosome Mapping/*methods/statistics & numerical data Computer Simulation Gene Frequency/genetics Genetic Diseases, Inborn/*genetics Genetic Markers/genetics Genotype Haplotypes/genetics Humans Linkage (Genetics)/*genetics Lod Score Markov Chains Matched-Pair Analysis Models, Genetic Mutation/*genetics *Nuclear Family *Research Design Sensitivity and SpecificityAprThe identification of genes contributing to complex diseases and quantitative traits requires genetic data of high fidelity, because undetected errors and mutations can profoundly affect linkage information. The recent emphasis on the use of the sibling-pair design eliminates or decreases the likelihood of detection of genotyping errors and marker mutations through apparent Mendelian incompatibilities or close double recombinants. In this article, we describe a hidden Markov method for detecting genotyping errors and mutations in multilocus linkage data. Specifically, we calculate the posterior probability of genotyping error or mutation for each sibling-pair-marker combination, conditional on all marker data and an assumed genotype-error rate. The method is designed for use with sibling-pair data when parental genotypes are unavailable. Through Monte Carlo simulation, we explore the effects of map density, marker-allele frequencies, marker position, and genotype-error rate on the accuracy of our error-detection method. In addition, we examine the impact of genotyping errors and error detection and correction on multipoint linkage information. We illustrate that even moderate error rates can result in substantial loss of linkage information, given efforts to fine-map a putative disease locus. Although simulations suggest that our method detects A, p.Arg381Gln) confers strong protection against Crohn's disease, and additional noncoding IL23R variants are independently associated. Replication studies confirmed IL23R associations in independent cohorts of patients with Crohn's disease or ulcerative colitis. These results and previous studies on the proinflammatory role of IL-23 prioritize this signaling pathway as a therapeutic target in inflammatory bowel disease.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17068223 K1095-9203 (Electronic) Journal Article Research Support, N.I.H., Extramural17068223Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, School of Medicine, University of Pittsburgh, University of Pittsburgh Medical Center Presbyterian, Mezzanine Level, C-Wing, 200 Lothrop Street, Pittsburgh, PA 15213, USA.? Duffy, D.L. 1997uBINNING 0.96: a program to call alleles based on approximate length data. (http://www.qimr.edu.au/davidD/binning.htm)~? Duffy, D. L.2006.An integrated genetic map for linkage analysis4-6 Behav Genet361qChromosome Mapping/*methods Genetic Markers Humans *Linkage (Genetics) Recombination, Genetic Regression AnalysisJanHere I describe an Internet accessible database containing interpolated genetic map positions for 12917 marker loci. These are estimated via locally weighted linear regression (loess) from the Build 35.1 physical map position and the linkage map of Kong, X., and coworkers (2004) Am. J. Hum. Genet. 75:1143-1148. For the pseudoautosomal region, I have interpolated a male map based on the sperm typing data of Lien, S., and coworkers (2000) Am. J. Hum. Genet. 66:557-566.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16523245 !0001-8244 (Print) Journal Article16523245^Queensland Institute of Medical Research, Brisbane Herston, Australia. David.Duffy@qimr.edu.au A~?*Durner, M. Vieland, V. J. Greenberg, D. A.1999ZFurther evidence for the increased power of LOD scores compared with nonparametric methods281-9Am J Hum Genet641lGenes, Dominant Genes, Recessive Humans *Lod Score *Models, Genetic Nuclear Family Statistics, NonparametricJanIn genetic analysis of diseases in which the underlying model is unknown, "model free" methods-such as affected sib pair (ASP) tests-are often preferred over LOD-score methods, although LOD-score methods under the correct or even approximately correct model are more powerful than ASP tests. However, there might be circumstances in which nonparametric methods will outperform LOD-score methods. Recently, Dizier et al. reported that, in some complex two-locus (2L) models, LOD-score methods with segregation analysis-derived parameters had less power to detect linkage than ASP tests. We investigated whether these particular models, in fact, represent a situation that ASP tests are more powerful than LOD scores. We simulated data according to the parameters specified by Dizier et al. and analyzed the data by using a (a) single locus (SL) LOD-score analysis performed twice, under a simple dominant and a recessive mode of inheritance (MOI), (b) ASP methods, and (c) nonparametric linkage (NPL) analysis. We show that SL analysis performed twice and corrected for the type I-error increase due to multiple testing yields almost as much linkage information as does an analysis under the correct 2L model and is more powerful than either the ASP method or the NPL method. We demonstrate that, even for complex genetic models, the most important condition for linkage analysis is that the assumed MOI at the disease locus being tested is approximately correct, not that the inheritance of the disease per se is correctly specified. In the analysis by Dizier et al., segregation analysis led to estimates of dominance parameters that were grossly misspecified for the locus tested in those models in which ASP tests appeared to be more powerful than LOD-score analyses.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9915967 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9915967cDepartment of Psychiatry, Mount Sinai Medical Center, New York, USA. martina@shallot.salad.mssm.edu~?NDurrant, C. Zondervan, K. T. Cardon, L. R. Hunt, S. Deloukas, P. Morris, A. P.2004bLinkage disequilibrium mapping via cladistic analysis of single-nucleotide polymorphism haplotypes35-43Am J Hum Genet751*Chromosome Mapping Computer Simulation Genetics, Population Haplotypes/*genetics Humans *Linkage Disequilibrium *Models, Genetic Polymorphism, Single Nucleotide/*geneticsJul/We present a novel approach to disease-gene mapping via cladistic analysis of single-nucleotide polymorphism (SNP) haplotypes obtained from large-scale, population-based association studies, applicable to whole-genome screens, candidate-gene studies, or fine-scale mapping. Clades of haplotypes are tested for association with disease, exploiting the expected similarity of chromosomes with recent shared ancestry in the region flanking the disease gene. The method is developed in a logistic-regression framework and can easily incorporate covariates such as environmental risk factors or additional unlinked loci to allow for population structure. To evaluate the power of this approach to detect disease-marker association, we have developed a simulation algorithm to generate high-density SNP data with short-range linkage disequilibrium based on empirical patterns of haplotype diversity. The results of the simulation study highlight substantial gains in power over single-locus tests for a wide range of disease models, despite overcorrection for multiple testing.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15148658 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't15148658WWellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom. ~?eDutta, S. Sinha, S. Chattopadhyay, A. Gangopadhyay, P. K. Mukhopadhyay, J. Singh, M. Mukhopadhyay, K.2005kCystathionine beta-synthase T833C/844INS68 polymorphism: a family-based study on mentally retarded children25Behav Brain Funct1BACKGROUND: Cystathionine beta-synthase (CBS) mediates conversion of homocysteine to cystathionine and deficiency in enzyme activity may lead to hyperhomocysteinemia/homocystinuria, which are often associated with mental retardation (MR). A large number of polymorphisms have been reported in the CBS gene, some of which impair its activity and among these, a T833C polymorphism in cis with a 68 bp insertion at 844 in the exon 8 is found to be associated with mild hyperhomocysteinemia in different ethnic groups. METHODS: The present study is aimed at investigating the association between T833C/844ins68 polymorphism and MR. One hundred and ninety MR cases were recruited after psychometric evaluation. Hundred and thirty-eight control subjects, two hundred and sixty-seven parents of MR probands and thirty cardiovascular disorder (CVD) patients were included for comparison. Peripheral blood was collected after obtaining informed written consent. The T833C/844ins68 polymorphism was investigated by PCR amplification of genomic DNA and restriction fragment length polymorphism analysis, followed by statistical analysis. RESULTS: The genotypic distribution of the polymorphism was within the Hardy-Weinberg equilibrium. A slightly increased genotypic frequency was observed in the Indian control population as compared to other Asian populations. Both haplotype-based haplotype relative risk analysis and transmission disequilibrium test reveled lack of association of the T833C/844ins68 polymorphism with MR; nevertheless, the relative risk calculated was higher (>1) and in a limited number of informative MR families, preferential transmission of the double mutant from heterozygous mothers to the MR probands was noticed (chi2 = 4.00, P < 0.05). CONCLUSION: This is the first molecular genetic study of CBS gene dealing with T833C/844ins68 double mutation in MR subjects. Our preliminary data indicate lack of association between T833C/844ins68 polymorphism with MR. However, higher relative risk and biased transmission of the double mutation from heterozygous mothers to MR probands are indicative of a risk of association between this polymorphism with mental retardation.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16375773 &1744-9081 (Electronic) Journal Article16375773fManovikas Biomedical Research and Diagnostic Centre, E,M, Bypass, Kolkata, India. mikpal2000@yahoo.com ~?Eaves, L. Erkanli, A.2003Markov Chain Monte Carlo approaches to analysis of genetic and environmental components of human developmental change and G x E interaction279-99 Behav Genet333Child *Child Development Computer Simulation Environment Family Genotype Humans Markov Chains *Models, Genetic Monte Carlo Method Twins/geneticsMay'The linear structural model has provided the statistical backbone of the analysis of twin and family data for 25 years. A new generation of questions cannot easily be forced into the framework of current approaches to modeling and data analysis because they involve nonlinear processes. Maximizing the likelihood with respect to parameters of such nonlinear models is often cumbersome and does not yield easily to current numerical methods. The application of Markov Chain Monte Carlo (MCMC) methods to modeling the nonlinear effects of genes and environment in MZ and DZ twins is outlined. Nonlinear developmental change and genotype x environment interaction in the presence of genotype-environment correlation are explored in simulated twin data. The MCMC method recovers the simulated parameters and provides estimates of error and latent (missing) trait values. Possible limitations of MCMC methods are discussed. Further studies are necessary explore the value of an approach that could extend the horizons of research in developmental genetic epidemiology.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12837018 F0001-8244 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.12837018Virginia Institute for Psychiatric and Behavioral Genetics, Department of Human Genetics, Virginia Commonwealth University, PO Box 980003, Richmond, Virginia 23298-0003, USA.U~?BEaves, L. Erkanli, A. Silberg, J. Angold, A. Maes, H. H. Foley, D.2005uApplication of Bayesian inference using Gibbs sampling to item-response theory modeling of multi-symptom genetic data765-80 Behav Genet356}Adolescent Bayes Theorem Female Humans Markov Chains *Models, Genetic Models, Statistical Monte Carlo Method Sampling StudiesNovpSeveral "genetic" item-response theory (IRT) models are fitted to the responses of 1086 adolescent female twins to the 33 multi-category item Mood and Feeling Questionnaire relating to depressive symptomatology in adolescence. A Markov-chain Monte Carlo (MCMC) algorithm is used within a Bayesian framework for inference using Gibbs sampling, implemented in the program WinBUGS 1.4. The final model incorporated separate genetic and non-shared environmental traits ("A and E") and item-specific genetic effects. Simpler models gave markedly poorer fit to the observations judged by the deviance information criterion (DIC). The common genetic factor showed major loadings on melancholic items, while the environmental factor loaded most highly on items relating to self-deprecation. The MCMC approach provides a convenient and flexible alternative to Maximum Likelihood for estimating the parameters of IRT models for relatively large numbers of items in a genetic context. Additional benefits of the IRT approach are discussed including the estimation of latent trait scores, including genetic factor scores, and their sampling errors.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16273316 F0001-8244 (Print) Journal Article Research Support, N.I.H., Extramural16273316Virginia Institute for Psychiatric and Behavioral Genetics, Department of Human Genetics, Virginia Commonwealth University, Richmond, VA 23298-0003, USA. eaves@mail2.vcu.edu ~?Eaves, L. Meyer, J.1994dLocating human quantitative trait loci: guidelines for the selection of sibling pairs for genotyping443-55 Behav Genet245Alleles *Chromosome Mapping Genes, Dominant Genes, Recessive Genetic Markers/genetics *Genotype Humans Models, Genetic Phenotype *Selection (Genetics)SepXSimulation studies were conducted to assess the relative merits of different nonrandom sampling strategies for the selection of sibling pairs for genotyping in the attempt to locate individual loci (QTLs) contributing to variation in human quantitative traits. For a constant amount of variation contributed by a QTL (25% of the total) the frequencies and dominance relationships of a trait increasing allele were varied. Three strategies for selection of pairs for genotyping were based on the phenotypic values of the siblings: "Concordant sib pairs" (CSP) are pairs in which both individuals exceed a given threshold value; "discordant sib pairs" (DSP) are pairs in which one member exceeds a given upper threshold and the other is below a specified lower threshold; and "most similar pairs" (MSP) are pairs selected for falling below a specified percentile ranking of the within-pair mean square for the quantitative trait. Tests for linkage with markers at 1, 2, 5, 10, and 20 cM from each of the QTLs were conducted for each of the selected samples and compared with tests based on the regression, in the entire sample, of within pair variation on the proportion of alleles identical by descent (IBD) at each marker locus. Tests for the effect of the increasing allele at the QTL ("candidate gene") were also conducted for the DSP pairs. No single nonrandom selection procedure yields as much as half the information realized in the total sample. However, a combined strategy which involves genotyping the 5% of MSP and DSP for the upper and lower quintiles of values of the quantitative trait (a further 3% of the sample approximately) yields lod scores which are usually more than 65% of the values realized for the entire sample. Tests comparing the proportion of increasing alleles in high- and low-scoring siblings from DSP samples are uniformly very powerful for detecting candidate loci. Even when it is not possible to measure the entire range of the phenotype with uniform precision, some attempt to differentiate among individuals in a common "unaffected" class of individuals can lead to considerable increase in power.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7993321 !0001-8244 (Print) Journal Article7993321TDepartment of Human Genetics, Virginia Commonwealth University, Richmond 23298-0003.~? Eaves, L. J.1973mThe structure of genotypic and environmental covariation for personality measurements: an analysis of the PEN275-82Br J Soc Clin Psychol123*Environment Extraversion (Psychology) Female Genetics, Behavioral *Genotype Humans Male Neurotic Disorders *Personality Assessment Phenotype Pregnancy Psychotic Disorders Questionnaires TwinsSepehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=4796046 !0007-1293 (Print) Journal Article4796046|7Eaves, L. J. Brumpton, R. J.1972+Factors of Covariation in Nicotiana-Rustica151-&Heredity29Oct://A1972N951000002.N9510 Times Cited:17 Cited References Count:41 0018-067XISI:A1972N951000002English~?Eaves, L. J. Gale, J. S.19747A method for analyzing the genetic basis of covariation253-67 Behav Genet43bAlleles Environment Female Genes, Dominant Humans Pregnancy Statistics Twins *Variation (Genetics)Sepehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=4471972 !0001-8244 (Print) Journal Article4471972~?3Eaves, L. J. Last, K. A. Young, P. A. Martin, N. G.1978;Model-fitting approaches to the analysis of human behaviour249-320Heredity413Adoption Environment Female *Genetics, Behavioral *Genetics, Medical Genotype Humans Mathematics *Models, Biological Pedigree Phenotype Pregnancy TwinsDecModel-fitting methods are now prominent in the analysis of human behavioural variation. Various ways of specifying models have been proposed. These are identical in their simplest form but differ in the emphasis given to more subtle sources of variation. The biometrical genetical approach allows flexibility in the specification of non-additive factors. Given additivity, the approach of path analysis may be used to specify several environmental models in the presence of assortative mating. In many cases the methods should yield identical conclusions. Several statistical methods have been proposed for parameter estimation and hypothesis testing. The most suitable rely on the method of maximum likelihood for the estimation of variance and covariance components. Any multifactorial model can be formulated in these terms. The choice of method will depend chiefly on the design of the experiment and the ease with which a data summary can be obtained without significant loss of information. Examples are given in which the causes of variation show different degrees of detectable complexity. A variety of experimental designs yield behavioural data which illustrate the contribution of additive and non-additive genetical effects, the mating system, sibling and cultural effects, the interaction of genetical effects with age and sex. The discrimination between alternative hypotheses is often difficult. The extension of the approach to the analysis of multiple measurements and discontinuous traits is considered.dhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=370072 (0018-067X (Print) Journal Article Review370072?'Eaves, L.J. Martin, N.G. Eysenck, S.B.G1977TA psychogenetical analysis of the covariance structure in four impulsiveness factors185-197.)Br. J. Math. Stat. Psychological Bulletin30?http://genepi.qimr.edu.au/contents/p/staff/nickM/1977/CV010.pdf~?"Eaves, L. J. Neale, M. C. Maes, H.1996CMultivariate multipoint linkage analysis of quantitative trait loci519-25 Behav Genet265Animals *Chromosome Mapping Factor Analysis, Statistical Female Genetic Markers/genetics *Genotype Humans Linkage (Genetics)/*genetics Male *Models, Genetic Multivariate Analysis Pedigree PhenotypeSepResolution of the genetic components of complex disorders may require simultaneous analysis of the contribution of individual quantitative trait loci (QTLs) to multiple variables. A likelihood approach is used to illustrate how the complexities of multivariate data may be resolved with multipoint linkage analysis. Sibling pair data were simulated from a model in which two QTLs and trait-specific polygenic effects explained all the sibling resemblance within and between five variables. Multipoint linkage analysis was used to obtain individual pair probabilities of having zero, one, or two alleles identical by descent, and these probabilities were applied in a weighted maximum-likelihood fit function. The results were compared with those obtained using conventional linear structural equation modeling to estimate the contribution of latent genetic factors to the genetic covariance in the multiple measures. Both analyses were conducted using the Mx package. Relatively poor agreement was found between genetic factors defined in purely statistical terms by varimax rotation of the first two factors of the genetic covariance matrix and the structure obtained by fitting a model jointly to the phenotypic and the multipoint linkage data.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8917951 g0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.8917951Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond 232928, USA. eaves@gems.vcu.edu V~?CEdwards, B. J. Haynes, C. Levenstien, M. A. Finch, S. J. Gordon, D.2005sPower and sample size calculations in the presence of phenotype errors for case/control genetic association studies18 BMC Genet61Alzheimer Disease/genetics Apolipoproteins E/genetics *Case-Control Studies *Diagnostic Errors Genetic Predisposition to Disease/*genetics Genetic Screening Humans *Models, Genetic Phenotype Sample SizeBACKGROUND: Phenotype error causes reduction in power to detect genetic association. We present a quantification of phenotype error, also known as diagnostic error, on power and sample size calculations for case-control genetic association studies between a marker locus and a disease phenotype. We consider the classic Pearson chi-square test for independence as our test of genetic association. To determine asymptotic power analytically, we compute the distribution's non-centrality parameter, which is a function of the case and control sample sizes, genotype frequencies, disease prevalence, and phenotype misclassification probabilities. We derive the non-centrality parameter in the presence of phenotype errors and equivalent formulas for misclassification cost (the percentage increase in minimum sample size needed to maintain constant asymptotic power at a fixed significance level for each percentage increase in a given misclassification parameter). We use a linear Taylor Series approximation for the cost of phenotype misclassification to determine lower bounds for the relative costs of misclassifying a true affected (respectively, unaffected) as a control (respectively, case). Power is verified by computer simulation. RESULTS: Our major findings are that: (i) the median absolute difference between analytic power with our method and simulation power was 0.001 and the absolute difference was no larger than 0.011; (ii) as the disease prevalence approaches 0, the cost of misclassifying a unaffected as a case becomes infinitely large while the cost of misclassifying an affected as a control approaches 0. CONCLUSION: Our work enables researchers to specifically quantify power loss and minimum sample size requirements in the presence of phenotype errors, thereby allowing for more realistic study design. For most diseases of current interest, verifying that cases are correctly classified is of paramount importance.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15819990 &1471-2156 (Electronic) Journal Article15819990kLaboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA. brian.edwards@yale.eduv?Efron, B. Tibshirani, R 1993!An Introduction to the Bootstrap. New York, NY.Chapman and Hall~?Efron, B. Tibshirani, R.2002AEmpirical bayes methods and false discovery rates for microarrays70-86Genet Epidemiol231*Bayes Theorem Breast Neoplasms/genetics Female Genes, BRCA1 Genes, BRCA2 Humans *Oligonucleotide Array Sequence Analysis Reproducibility of ResultsJunIn a classic two-sample problem, one might use Wilcoxon's statistic to test for a difference between treatment and control subjects. The analogous microarray experiment yields thousands of Wilcoxon statistics, one for each gene on the array, and confronts the statistician with a difficult simultaneous inference situation. We will discuss two inferential approaches to this problem: an empirical Bayes method that requires very little a priori Bayesian modeling, and the frequentist method of "false discovery rates" proposed by Benjamini and Hochberg in 1995. It turns out that the two methods are closely related and can be used together to produce sensible simultaneous inferences.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12112249 !0741-0395 (Print) Journal Article12112249mDepartment of Statistics and Division of Biostatistics, Stanford University, Stanford, California 94305, USA.&~?Ehm, M. Wagner, M.1998;A test statistic to detect errors in sib-pair relationships181-8Am J Hum Genet621*Algorithms Female Genetic Diseases, Inborn/*genetics Genetic Markers *Genotype Humans *Linkage (Genetics) Male *Models, Statistical Nuclear Family ProbabilityJanSeveral authors have proposed algorithms to detect Mendelian errors in human genetic linkage data. Most currently available methods use likelihood-based methods on multiplex family data to identify typing or pedigree errors. These algorithms cannot be applied in many sib-pair collections, because of lack of parental-genotype information. Nonetheless, misspecifying the relationships between individuals has serious consequences for sib-pair linkage studies: false relationships bias the statistics designed to identify linkage with disease phenotypes. To test the hypothesis that two individuals are sibs, we propose a test statistic based on the summation, over a large number of genetic markers, of the number of alleles shared identical by state by a pair of individuals, for each marker. The test statistic has an approximately normal distribution under the null hypothesis, and extreme negative values correspond to nonsib pairs. Power and significance studies show that the test statistic calculated by use of 50 unlinked markers has 96% power to detect half-sibs and has 100% power to detect unrelated individuals as not full-sib pairs, with a 5% false-positive rate. Furthermore, extreme positive values of the test statistic identify sibs as MZ twins.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9443861 !0002-9297 (Print) Journal Article9443861mBioinformatics Group, Glaxo Wellcome, Inc., Research Triangle Park, NC 27709, USA. mge37216@glaxowellcome.com$~?Ekstrom, C. T.2004EMultipoint linkage analysis of quantitative traits on sex-chromosomes218-30Genet Epidemiol263Algorithms Chromosome Mapping/*methods Female Gene Frequency Genetic Markers Humans Linkage (Genetics) Male Markov Chains Models, Genetic Phenotype Quantitative Trait Loci/*genetics Regression Analysis Sex Chromosomes/*geneticsAprVariance component models form a powerful and flexible tool for multipoint linkage analysis of quantitative traits. Estimates of genetic similarity are needed for the variance component model to detect linkage and to locate genes, and two methods are commonly used to calculate multipoint identity-by-descent (IBD) estimates for autosomes. Fulker et al. ([1995] Am. J. Hum. Genet. 56: 1229-1233) and Almasy and Blangero ([1998] Am. J. Hum. Genet. 62: 119-121) used multiple regression to estimate the IBD sharing along a chromosome, while the approach of Kruglyak and Lander ([1995] Am. J. Hum. Genet. 57: 439-454) is based on a hidden Markov model. In this paper, we modify the variance component model to accommodate sex-chromosomes, and we extend both multipoint IBD estimation methods to accommodate sex-linked loci. Simulation studies demonstrate the power and precision of the variance component model to detect QTLs located on the sex-chromosome. The two multipoint IBD estimation methods have the same accuracy to identify QTL position, but the hidden Markov model yields a larger average maximum LOD score to detect linkage than the regression model. The extension of the multipoint IBD estimation methods and the variance component model to the X chromosome shows that the variance component model is a powerful and flexible tool for linkage analysis of quantitative traits on both autosomes and sex-chromosomes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15022208 !0741-0395 (Print) Journal Article15022208}Department of Mathematics and Physics, Royal Veterinary and Agricultural University, Copenhagen, Denmark. ekstrom@dina.kvl.dk~?4Elston, R. C. Buxbaum, S. Jacobs, K. B. Olson, J. M.2000Haseman and Elston revisited1-17Genet Epidemiol191Alleles Computer Simulation Genetic Diseases, Inborn/*genetics Genetic Markers Humans Linear Models *Linkage (Genetics) *Models, Statistical Nuclear Family Polymorphism, Genetic Variation (Genetics)JulHaseman and Elston (H-E) [1972] proposed a method to detect quantitative trait loci by linkage to a marker. The squared sib-pair trait difference is regressed on the proportion of marker alleles the pair is estimated to share identical by descent: a significantly negative regression coefficient suggests linkage. It has been shown that a maximum likelihood method that directly models the sib-pair covariance has more power. This increase in power can also be obtained using the H-E regression procedure by changing the dependent variable from the squared difference to the mean-corrected product of the sibs' trait values. Multiple sibs in a sibship can be accommodated by allowing for the correlations between pairs of products in a generalized least squares procedure. Multiple trait loci, including epistatic interactions, involve only multiple linear regression. Multivariate traits can use the method of Amos et al. [1990] to find the linear function of the traits that maximizes the evidence for linkage, which now leads more simply to a test of significance. Multiple markers can be the basis of a multipoint analysis. Results of simulation studies for a continuous trait are presented that investigate Type I error and power. A similar general scheme can be used to study affected sib pairs, testing whether their identity by descent sharing probabilities are greater than would be expected in the absence of linkage, and to study other types of relative pairs.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10861893 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10861893Department of Epidemiology and Biostatistics, Rammelkamp Center for Education and Research, MetroHealth Campus, Case Western Reserve University, Cleveland, Ohio 44109-1998, USA. rce@darwin.cwru.edu~?Elston, R. C. Stewart, J.19719A general model for the genetic analysis of pedigree data523-42 Hum Hered216Breeding Chromosomes Counseling Genes Genotype Humans Linkage (Genetics) Mathematics *Models, Biological *Pedigree Phenotype Probability Sex Chromosomesehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=5149961 !0001-5652 (Print) Journal Article5149961? Emigh, T.H.19805A comparison of tests for Hardy-Weinberg equilibrium.627-642 Biometrics 36Vhttp://links.jstor.org/sici?sici=0006-341X(198012)36%3A4%3C627%3AACOTFH%3E2.0.CO%3B2-S ~?'Epstein, M. P. Duren, W. L. Boehnke, M.2000;Improved inference of relationship for pairs of individuals1219-31Am J Hum Genet675Alleles Chromosome Mapping/*methods Computer Simulation Diabetes Mellitus, Type 2/genetics Female Gene Frequency/genetics Genetic Markers/genetics Genetic Screening Genotype Humans Likelihood Functions Linkage (Genetics)/genetics Male *Matched-Pair Analysis Models, Genetic Multicenter Studies Nuclear Family Pedigree Research Design Software Twins, Monozygotic X Chromosome/geneticsNovLinkage analyses of genetic diseases and quantitative traits generally are performed using family data. These studies assume the relationships between individuals within families are known correctly. Misclassification of relationships can lead to reduced or inappropriately increased evidence for linkage. Boehnke and Cox (1997) presented a likelihood-based method to infer the most likely relationship of a pair of putative sibs. Here, we modify this method to consider all possible pairs of individuals in the sample, to test for additional relationships, to allow explicitly for genotyping error, and to include X-linked data. Using autosomal genome scan data, our method has excellent power to differentiate monozygotic twins, full sibs, parent-offspring pairs, second-degree (2 degrees ) relatives, first cousins, and unrelated pairs but is unable to distinguish accurately among the 2 degrees relationships of half sibs, avuncular pairs, and grandparent-grandchild pairs. Inclusion of X-linked data improves our ability to distinguish certain types of 2 degrees relationships. Our method also models genotyping error successfully, to judge by the recovery of MZ twins and parent-offspring pairs that are otherwise misclassified when error exists. We have included these extensions in the latest version of our computer program RELPAIR and have applied the program to data from the Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus (FUSION) study.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11032786 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11032786NDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA. ~?"Epstein, M. P. Lin, X. Boehnke, M.2003MA tobit variance-component method for linkage analysis of censored trait data611-20Am J Hum Genet723Analysis of Variance *Chromosome Mapping/methods Chromosomes, Human Humans *Linkage (Genetics) *Models, Genetic Models, Statistical *Quantitative Trait LociMarvVariance-component (VC) methods are flexible and powerful procedures for the mapping of genes that influence quantitative traits. However, traditional VC methods make the critical assumption that the quantitative-trait data within a family either follow or can be transformed to follow a multivariate normal distribution. Violation of the multivariate normality assumption can occur if trait data are censored at some threshold value. Trait censoring can arise in a variety of ways, including assay limitation or confounding due to medication. Valid linkage analyses of censored data require the development of a modified VC method that directly models the censoring event. Here, we present such a model, which we call the "tobit VC method." Using simulation studies, we compare and contrast the performance of the traditional and tobit VC methods for linkage analysis of censored trait data. For the simulation settings that we considered, our results suggest that (1) analyses of censored data by using the traditional VC method lead to severe bias in parameter estimates and a modest increase in false-positive linkage findings, (2) analyses with the tobit VC method lead to unbiased parameter estimates and type I error rates that reflect nominal levels, and (3) the tobit VC method has a modest increase in linkage power as compared with the traditional VC method. We also apply the tobit VC method to censored data from the Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics study and provide two examples in which the tobit VC method yields noticeably different results as compared with the traditional method.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12587095 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.12587095dDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, USA. mepstein@genetics.emory.edu ~?Epstein, M. P. Satten, G. A.2003SInference on haplotype effects in case-control studies using unphased genotype data1316-29Am J Hum Genet736Algorithms Case-Control Studies Diabetes Mellitus, Type 2/genetics *Genetic Predisposition to Disease Haplotypes/*genetics Humans Likelihood Functions *Models, Genetic Polymorphism, Single Nucleotide/genetics Risk FactorsDecA variety of statistical methods exist for detecting haplotype-disease association through use of genetic data from a case-control study. Since such data often consist of unphased genotypes (resulting in haplotype ambiguity), such statistical methods typically apply the expectation-maximization (EM) algorithm for inference. However, the majority of these methods fail to perform inference on the effect of particular haplotypes or haplotype features on disease risk. Since such inference is valuable, we develop a retrospective likelihood for estimating and testing the effects of specific features of single-nucleotide polymorphism (SNP)-based haplotypes on disease risk using unphased genotype data from a case-control study. Our proposed method has a flexible structure that allows, among other choices, modeling of multiplicative, dominant, and recessive effects of specific haplotype features on disease risk. In addition, our method relaxes the requirement of Hardy-Weinberg equilibrium of haplotype frequencies in case subjects, which is typically required of EM-based haplotype methods. Also, our method easily accommodates missing SNP information. Finally, our method allows for asymptotic, permutation-based, or bootstrap inference. We apply our method to case-control SNP genotype data from the Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus (FUSION) Genetics study and identify two haplotypes that appear to be significantly associated with type 2 diabetes. Using the FUSION data, we assess the accuracy of asymptotic P values by comparing them with P values obtained from a permutation procedure. We also assess the accuracy of asymptotic confidence intervals for relative-risk parameters for haplotype effects, by a simulation study based on the FUSION data.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14631556 !0002-9297 (Print) Journal Article14631556dDepartment of Human Genetics, Emory University, Atlanta, GA, 30322, USA. mepstein@genetics.emory.edu:~? Evans, D. M.2002uThe power of multivariate quantitative-trait loci linkage analysis is influenced by the correlation between variables1599-602Am J Hum Genet706Chi-Square Distribution Chromosome Mapping/*methods/*statistics & numerical data Environment Humans Linkage (Genetics)/*genetics Models, Genetic Nuclear Family *Quantitative Trait, Heritable Variation (Genetics)/geneticsJunfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11992269 0002-9297 (Print) Comment Letter11992269~?Evans, D. M. Cardon, L. R.2005xA comparison of linkage disequilibrium patterns and estimated population recombination rates across multiple populations681-7Am J Hum Genet764?African Americans/genetics African Continental Ancestry Group/genetics Asian Continental Ancestry Group/genetics Chromosome Mapping Chromosomes, Human, Pair 20 European Continental Ancestry Group/genetics *Genetics, Population Great Britain Haplotypes Humans *Linkage Disequilibrium Recombination, Genetic United StatesApr(Large-scale studies of linkage disequilibrium (LD) have shown considerable variation in the extent and distribution of pairwise LD within and between populations. Taken at face value, these results suggest that genomewide LD maps for one population may not be generalizable to other populations. However, at least part of this diversity is due to some undesirable features of pairwise LD measures, which are well documented for the D' and r2 measures. In this report, we compare patterns of LD derived from pairwise measures with statistical estimates of population recombination rates ( rho ) along a 10-Mb stretch of chromosome 20 in four population samples, comprising East Asians, African Americans, and U.K. and U.S. individuals of western European descent. The results reveal the expected variability of D' within and between populations but show better concordance in estimates of r2 for the same markers across the population samples. Estimates of rho correlate well across populations, but there is still evidence of population-specific spikes and troughs in rho values. We conclude that it is unlikely that a single haplotype map will provide a definitive guide for association studies of many populations; rather, multiple maps will need to be constructed to provide the best-possible guides for gene mapping.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15719321 y0002-9297 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15719321lWellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom. davide@well.ox.ac.uk~?Evans, D. M. Cardon, L. R.20069Genome-wide association: a promising start to a long race350-4 Trends Genet227Chromosome Mapping/methods Female *Genome, Human Humans *Linkage (Genetics) Male Models, Genetic *Phenotype Quantitative Trait Loci/*geneticsJulA recent study by Cheung et al. demonstrates how to identify expression quantitative trait loci (eQTLs) underlying gene expression phenotypes through a combination of genome-wide linkage analysis and subsequent fine mapping or by genome-wide association (GWA) analysis. This study emphasizes the complexity of human traits, highlighting the challenges faced by investigators--in particular, insufficient linkage disequilibrium between the trait and marker variant, genetic heterogeneity and correcting for multiple testing will all adversely impact the power to detect loci by association. These issues must be considered carefully if the GWA approach is to succeed in mapping complex phenotypes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16713652 !0168-9525 (Print) Journal Article16713652iThe Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK.~?Evans, D. M. Medland, S. E.2003mA note on including phenotypic information from monozygotic twins in variance components QTL linkage analysis613-7 Ann Hum Genet67Pt 6Humans Linkage (Genetics)/*genetics *Models, Genetic Pedigree *Phenotype *Quantitative Trait Loci Research Design Twins/*geneticsNov=Williams & Blangero (1999) derived closed form expressions for the power of a univariate variance components test of linkage for a variety of pedigree structures. We have extended their results by investigating the effect of including monozygotic twins in the design on the power to detect linkage. Specifically, we determined the power associated with a pedigree of size three, where individuals one and two were monozygotic twins and individual three was a full sibling to the twins. The power of this sampling unit was uniformly greater than the power obtained from a sib-pair under the same genetic model. The reason for this was that addition of a second monozygotic twin provided another estimate of the sibling correlation for the particular IBD class. In addition, when the total heritability of the trait was <50%, the number of individuals that needed to be phenotyped was less than that with sib-pairs alone. However, a pedigree consisting of a monozygotic pair and sibling was never as informative as a sib-trio, presumably because the sib-trio provided information about allele sharing between three individuals, whereas the monozygotic twins and sibling unit only provided one such relationship. We conclude that including a monozygotic twin in the analysis is an economical strategy, since only one twin needs to be genotyped.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14641249 30003-4800 (Print) Comparative Study Journal Article14641249Queensland Institute of Medical Research and Joint Genetics Program, University of Queensland, Brisbane, Australia. davide@well.ox.ac.uksO?!Evans, M. Hastings, N. Peacock, B2000Statistical Distributions, New York, NY.Wiley3rdm?Everitt, B.S. Hand, D.J. 1981Finite mixture distributions.LondonChapman and Hallr?Ewens, W.J. Grant, G.R2001&Statistical Methods in Bioinformatics. New York, NY.Springer"~?Ewens, W. J. Spielman, R. S.1995IThe transmission/disequilibrium test: history, subdivision, and admixture455-64Am J Hum Genet572oAlleles Genetic Diseases, Inborn/*genetics Humans *Linkage Disequilibrium *Models, Genetic *Models, StatisticalAugDisease association with a genetic marker is often taken as a preliminary indication of linkage with disease susceptibility. However, population subdivision and admixture may lead to disease association even in the absence of linkage. In a previous paper, we described a test for linkage (and linkage disequilibrium) between a genetic marker and disease susceptibility; linkage is detected by this test only if association is also present. This transmission/disequilibrium test (TDT) is carried out with data on transmission of marker alleles from parents heterozygous for the marker to affected offspring. The TDT is a valid test for linkage and association, even when the association is caused by population subdivision and admixture. In the previous paper, we did not explicitly consider the effect of recent history on population structure. Here we extend the previous results by examining in detail the effects of subdivision and admixture, viewed as processes in population history. We describe two models for these processes. For both models, we analyze the properties of (a) the TDT as a test for linkage (and association) between marker and disease and (b) the conventional contingency statistic used with family data to test for population association. We show that the contingency test statistic does not have a chi 2 distribution if subdivision or admixture is present. In contrast, the TDT remains a valid chi 2 statistic for the linkage hypothesis, regardless of population history.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7668272 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.7668272PDepartment of Biology, University of Pennsylvania, Philadelphia 19104-6018, USA.~?Excoffier, L. Slatkin, M.1995XMaximum-likelihood estimation of molecular haplotype frequencies in a diploid population921-7 Mol Biol Evol125Algorithms Animals Diploidy *Gene Frequency Haplotypes/*genetics Humans *Linkage (Genetics) Mathematics *Models, Genetic *Models, Statistical Monte Carlo Method Probability Regression AnalysisSepfMolecular techniques allow the survey of a large number of linked polymorphic loci in random samples from diploid populations. However, the gametic phase of haplotypes is usually unknown when diploid individuals are heterozygous at more than one locus. To overcome this difficulty, we implement an expectation-maximization (EM) algorithm leading to maximum-likelihood estimates of molecular haplotype frequencies under the assumption of Hardy-Weinberg proportions. The performance of the algorithm is evaluated for simulated data representing both DNA sequences and highly polymorphic loci with different levels of recombination. As expected, the EM algorithm is found to perform best for large samples, regardless of recombination rates among loci. To ensure finding the global maximum likelihood estimate, the EM algorithm should be started from several initial conditions. The present approach appears to be useful for the analysis of nuclear DNA sequences or highly variable loci. Although the algorithm, in principle, can accommodate an arbitrary number of loci, there are practical limitations because the computing time grows exponentially with the number of polymorphic loci. Although the algorithm, in principle, can accommodate an arbitrary number of loci, there are practical limitations because the computing time grows exponentially with the number of polymorphic loci.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7476138 g0737-4038 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.7476138>Department of Anthropology, University of Geneva, Switzerland.x?Falconer, D.S. Mackay, T.F.C.1996.Introduction to Quantitative Genetics,4th Edn.HarlowLongman~?Falk, C. T. Rubinstein, P.1987iHaplotype relative risks: an easy reliable way to construct a proper control sample for risk calculations227-33 Ann Hum Genet51Pt 3Diabetes Mellitus, Type 1/*genetics HLA-DR Antigens/genetics HLA-DR3 Antigen HLA-DR4 Antigen *Haplotypes Humans Models, Genetic Phenotype Risk FactorsJul0An alternative to Woolf's (1955) relative risk (RR) statistic is proposed for use in calculating the risk of disease in the presence of particular antigens or phenotypes. This alternative uses, as the control sample, the parental antigens or haplotypes not present in the affected child. The formulation of a haplotype relative risk (HRR) thus eliminates the problems of sampling from the same homogeneous population to form both the disease sample and an appropriate control. We show that, in families selected through a single affected individual, where transmission of the four parental haplotypes can be followed unambiguously, the mathematical expectation of the HRR is identical to that of the RR. Since the sample formed from the 'non-affected' parental haplotypes is clearly from the same population as the disease sample, the HRR thus provides a reliable alternative to the RR. A further advantage obtains when family data are being collected as part of a study since the control sample is then automatically contained in the family material. Data from studies of patients with insulin dependent diabetes mellitus (IDDM) are used to obtain an estimate of the risk to those with HLA antigens or phenotypes associated with IDDM using the HRR statistic. A comparison of the HRR's and RR's for these data is also presented.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3500674 F0003-4800 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.3500674HLindsley F. Kimball Research Institute, New York Blood Center, NY 10021.[~?TFan, J. B. Ma, J. Zhang, C. S. Tang, J. X. Gu, N. F. Feng, G. Y. St Clair, D. He, L.2003A family-based association study of T1945C polymorphism in the proline dehydrogenase gene and schizophrenia in the Chinese population252-4 Neurosci Lett3383China Chromosomes, Human, Pair 22/genetics Female Genetic Predisposition to Disease/ethnology *Genotype Humans Male Polymorphism, Single Nucleotide/genetics Proline Oxidase/*genetics Schizophrenia/*geneticsMar 68Previous studies have reported genetic linkage evidence for a candidate gene of schizophrenia on chromosome 22q11 but no genes in this region have been really confirmed to be involved in the etiology of schizophrenia so far. Very recently, the proline dehydrogenase gene (PRODH), located in the most centromeric part of the 22q11 microdeletion region, has been reported to be strongly associated with schizophrenia from three sets of independent samples and the most significant evidence for association was derived from a single nucleotide polymorphism-PRODH*1945(T/C). We genotyped this polymorphism in 166 Chinese family trios with schizophrenia from East China. No evidence for preferential transmission of the PRODH*1945 alleles from parents to affected offsprings was found using either Transmission Disequilibrium Test (P=0.4) or Haplotype-based Haplotype Relative Risk analysis (P=0.35). Our results suggest that the 1945(T/C) polymorphism of the proline dehydrogenase gene is unlikely to play a major role in the susceptibility to schizophrenia in the Chinese population.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12581843 B0304-3940 (Print) Journal Article Research Support, Non-U.S. Gov't12581843xBio-X Life Science Research Center, PO Box 501, Hao Ran Building, Shanghai Jiao Tong University, Shanghai 200030, China.~?Faraway, J. J.1993>Improved sib-pair linkage test for disease susceptibility loci225-33Genet Epidemiol104qAlleles Genetic Markers *Genetic Predisposition to Disease Humans Linkage (Genetics)/*genetics Statistics/methodsAn improved sib-pair test for linkage is introduced which is superior to the previously proposed tests. The test is derived from the standard chi-squared goodness of fit statistic by restricting the alternative hypothesis to the genetically possible. Critical values are given and exact power comparisons are made with the previously proposed tests. The new test is shown to be more powerful for finite samples as well as being asymptotically uniformly most powerful.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8224803 !0741-0395 (Print) Journal Article8224803BDepartment of Statistics, University of Michigan, Ann Arbor 48109.[~?Farrer, L. A. Cupples, L. A. Haines, J. L. Hyman, B. Kukull, W. A. Mayeux, R. Myers, R. H. Pericak-Vance, M. A. Risch, N. van Duijn, C. M.1997Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium1349-56Jama27816Adult African Continental Ancestry Group/genetics Age Factors Aged Aged, 80 and over Alleles Alzheimer Disease/epidemiology/*genetics Apolipoproteins E/*genetics Asian Continental Ancestry Group/genetics Continental Population Groups/*genetics European Continental Ancestry Group/genetics Female Genotype Hispanic Americans/genetics Humans Logistic Models Male Middle Aged Risk Factors Oct 22-29 OBJECTIVE: To examine more closely the association between apolipoprotein E (APOE) genotype and Alzheimer disease (AD) by age and sex in populations of various ethnic and racial denominations. DATA SOURCES: Forty research teams contributed data on APOE genotype, sex, age at disease onset, and ethnic background for 5930 patients who met criteria for probable or definite AD and 8607 controls without dementia who were recruited from clinical, community, and brain bank sources. MAIN OUTCOME MEASURES: Odds ratios (ORs) and 95% confidence intervals (CIs) for AD, adjusted for age and study and stratified by major ethnic group (Caucasian, African American, Hispanic, and Japanese) and source, were computed for APOE genotypes epsilon2/epsilon2, epsilon2/epsilon3, epsilon2/epsilon4, epsilon3/epsilon4, and epsilon4/epsilon4 relative to the epsilon3/epsilon3 group. The influence of age and sex on the OR for each genotype was assessed using logistic regression procedures. RESULTS: Among Caucasian subjects from clinic- or autopsy-based studies, the risk of AD was significantly increased for people with genotypes epsilon2/epsilon4 (OR=2.6, 95% CI=1.6-4.0), epsilon3/epsilon4 (OR=3.2, 95% CI=2.8-3.8), and epsilon4/epsilon4 (OR=14.9, 95% CI= 10.8-20.6); whereas, the ORs were decreased for people with genotypes epsilon2/epsilon2 (OR=0.6, 95% CI=0.2-2.0) and epsilon2/epsilon3 (OR=0.6, 95% CI=0.5-0.8). The APOE epsilon4-AD association was weaker among African Americans and Hispanics, but there was significant heterogeneity in ORs among studies of African Americans (P<.03). The APOE epsilon4-AD association in Japanese subjects was stronger than in Caucasian subjects (epsilon3/epsilon4: OR=5.6, 95% CI=3.9-8.0; epsilon4/epsilon4: OR=33.1, 95% CI=13.6-80.5). The epsilon2/epsilon3 genotype appears equally protective across ethnic groups. We also found that among Caucasians, APOE genotype distributions are similar in groups of patients with AD whose diagnoses were determined clinically or by autopsy. In addition, we found that the APOE epsilon4 effect is evident at all ages between 40 and 90 years but diminishes after age 70 years and that the risk of AD associated with a given genotype varies with sex. CONCLUSIONS: The APOE epsilon4 allele represents a major risk factor for AD in all ethnic groups studied, across all ages between 40 and 90 years, and in both men and women. The association between APOE epsilon4 and AD in African Americans requires clarification, and the attenuated effect of APOE epsilon4 in Hispanics should be investigated further.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9343467 u0098-7484 (Print) Journal Article Meta-Analysis Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.9343467dDepartment of Neurology, Boston University School of Medicine, Mass 02118, USA. farrer@neugen.bu.edu~? Feingold, E.2001AMethods for linkage analysis of quantitative trait loci in humans167-80Theor Popul Biol603|Bayes Theorem Chromosome Mapping/*methods Humans *Linkage Disequilibrium *Models, Statistical *Quantitative Trait, HeritableNovThis paper reviews linkage analysis methods for detecting loci associated with quantitative traits in humans. All such methods are based on the underlying principle that family members who have similar trait values should have higher than expected levels of sharing of genetic material (identity by descent) near the genes that influence those traits. A number of different statistical methods for testing that association between shared trait values and shared identity by descent have been developed over the past 30 or more years. These different types of tests are reviewed here, with emphasis on their theory and derivations. Robustness and power are also discussed.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11855951 (0040-5809 (Print) Journal Article Review11855951~Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA.~? Feingold, E.2002ERegression-based quantitative-trait-locus mapping in the 21st century217-22Am J Hum Genet712Animals Chromosome Mapping/statistics & numerical data/*trends Forecasting Humans *Likelihood Functions Normal Distribution *Quantitative Trait, Heritable *Regression AnalysisAugfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12154779 H0002-9297 (Print) Comment Editorial Research Support, U.S. Gov't, P.H.S.12154779~?&Feingold, E. Brown, P. O. Siegmund, D.1993gGaussian models for genetic linkage analysis using complete high-resolution maps of identity by descent234-51Am J Hum Genet531*Humans *Linkage (Genetics) Models, GeneticJulGaussian-process models are developed to detect genetic linkage using complete high-resolution maps of identity by descent between affected relative pairs. Approximations are given for the significance level and power of the likelihood-ratio test of no linkage and for likelihood-ratio confidence regions for trait loci. The sample sizes required to detect linkage by using different classes of affected relative pairs are compared, and the problem of combining data from different classes of relatives is discussed.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8317489 k0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.83174898Department of Statistics, Stanford University, CA 94305.6~?NFernandez, J. R. Etzel, C. Beasley, T. M. Shete, S. Amos, C. I. Allison, D. B.2002\Improving the power of sib pair quantitative trait loci detection by phenotype winsorization59-67 Hum Hered532yGenetic Predisposition to Disease Genetic Techniques Models, Genetic Phenotype *Quantitative Trait, Heritable *StatisticsmOBJECTIVES: In sib pair studies, quantitative trait loci (QTL) identification may be adversely affected by non-normality in the phenotypic distribution, particularly when subjects falling in the tails of the distribution bias the trait mean or variance. We evaluated the robustness and power of reducing the influence of subjects with extreme phenotypic values by Winsorizing non-normal distributions in three versions of Haseman-Elston regression-based methods of QTL linkage analysis. METHODS: Data were simulated for normal and non-normal distributions. Phenotypic values that correspond to cutoff points at the omega and 1 - omega percentiles of the distribution were identified, and phenotypic values falling outside the boundaries of the omega and 1 - omega cutoff points were replaced by the omega and 1 - omega values, respectively. One million replications were performed for the three tests of linkage for Winsorized and non-Winsorized data. RESULTS: Winsorization reduced conservatism in the tails of the empirical type I error rate for the vast majority of the tests of linkage, increased the power of QTL detection in non-normal data and created a slight negative bias in symmetrical phenotypic distributions. CONCLUSIONS: Winsorizing can improve the power of QTL detection with certain non-normal distributions but can also introduce bias into the estimate of the QTL effect.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12037405 Y0001-5652 (Print) Evaluation Studies Journal Article Research Support, U.S. Gov't, P.H.S.12037405Department of Nutrition Sciences, Division of Physiology and Metabolism, The University of Alabama at Birmingham, 35294-3360, USA. fernandj@shrp.uab.edu~?Ferreira, M. A.2004VLinkage analysis: principles and methods for the analysis of human quantitative traits513-30Twin Res75Genotype Humans Linkage (Genetics)/*genetics Models, Genetic *Models, Statistical Phenotype Quantitative Trait Loci/*genetics *Quantitative Trait, HeritableOct>Currently, mapping genes for complex human traits relies on two complementary approaches, linkage and association analyses. Both suffer from several methodological and theoretical limitations, which can considerably increase the type-1 error rate and reduce the power to map human quantitative trait loci (QTL). This review focuses on linkage methods for QTL mapping. It summarizes the most common linkage statistics used, namely Haseman-Elston-based methods, variance components, and statistics that condition on trait values. Methods developed more recently that accommodate the X-chromosome, parental imprinting and allelic association in linkage analysis are also summarized. The type-I error rate and power of these methods are discussed. Finally, rough guidelines are provided to help guide the choice of linkage statistics.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15527667 B1369-0523 (Print) Journal Article Research Support, Non-U.S. Gov't15527667kQueensland Institute of Medical Research, Royal Brisbane Hospital, Brisbane, Australia. manuelF@qimr.edu.au Z~?Ferreira, M. A. O'Gorman, L. Le Souef, P. Burton, P. R. Toelle, B. G. Robertson, C. F. Visscher, P. M. Martin, N. G. Duffy, D. L.2005Robust estimation of experimentwise P values applied to a genome scan of multiple asthma traits identifies a new region of significant linkage on chromosome 20q131075-85Am J Hum Genet776Adult Aged Animals Asthma/*genetics/physiopathology Bronchial Hyperreactivity/genetics Chromosome Mapping Chromosomes, Human, Pair 12 *Chromosomes, Human, Pair 20 Dermatophagoides pteronyssinus/immunology Female Forced Expiratory Volume/genetics Genetic Markers *Genome, Human Humans Hypersensitivity, Immediate/genetics *Linkage (Genetics) Male Middle Aged *Probability Quantitative Trait Loci/*genetics Skin TestsDecQOver 30 genomic regions show linkage to asthma traits. Six asthma genes have been cloned, but the putative loci in many linked regions have not been identified. To search for asthma susceptibility loci, we performed genomewide univariate linkage analyses of seven asthma traits, using 202 Australian families ascertained through a twin proband. House-dust mite sensitivity (Dpter) exceeded the empirical threshold for significant linkage at 102 cM on chromosome 20q13, near marker D20S173 (empirical pointwise P = .00001 and genomewide P = .005, both uncorrected for multiple-trait testing). Atopy, bronchial hyperresponsiveness (BHR), and forced expiratory volume in 1 s (FEV1) were also linked to this region. In addition, 16 regions were linked to at least one trait at the suggestive level, including 12q24, which has consistently shown linkage to asthma traits in other studies. Some regions were expected to be false-positives arising from multiple-trait testing. To address this, we developed a new approach to estimate genomewide significance that accounts for multiple-trait testing and for correlation between traits and that does not require a Bonferroni correction. With this approach, Dpter remained significantly linked to 20q13 (empirical genomewide P = .042), and airway obstruction remained linked to 12q24 at the suggestive level. Finally, we extended this method to show that the linkage of Dpter, atopy, BHR, FEV1, asthma, and airway obstruction to chromosome 20q13 is unlikely to be due to chance and may result from a quantitative trait locus in this region that affects several of these traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16380917 30002-9297 (Print) Comparative Study Journal Article16380917RQueensland Institute of Medical Research, Brisbane, Australia. manuelF@qimr.edu.au? Fisher, R.A. 1918OThe correlation between relatives on the supposition of Mendelian inheritance. 399-433 Trans. R. Soc52Mhttp://genepi.qimr.edu.au/staff/nick_pdf/Classics/1918FscherRACorrelation.pdf? Fisher, R.A.1922:On the mathematical foundations of theoretical statistics.309-368!Phil. Trans. R. Soc. Lond. Ser. A222? Fisher, R.A.1934IThe effect of methods of ascertainment upon the estimation of frequencies13-256 Ann. Eugen.g? Fisher, R.A.1954(Statistical Methods for Research Workers New York, NY.Hafner~?8Flint-Garcia, S. A. Thornsberry, J. M. Buckler, E. S. th2003-Structure of linkage disequilibrium in plants357-74Annu Rev Plant Biol54dAnimals Humans Linkage Disequilibrium/*genetics Models, Genetic Plants/*genetics Species SpecificitykFuture advances in plant genomics will make it possible to scan a genome for polymorphisms associated with qualitative and quantitative traits. Before this potential can be realized, we must understand the nature of linkage disequilibrium (LD) within a genome. LD, the nonrandom association of alleles at different loci, plays an integral role in association mapping, and determines the resolution of an association study. Recently, association mapping has been exploited to dissect quantitative trait loci (QTL). With the exception of maize and Arabidopsis, little research has been conducted on LD in plants. The mating system of the species (selfing versus outcrossing), and phenomena such as population structure and recombination hot spots, can strongly influence patterns of LD. The basic patterns of LD in plants will be better understood as more species are analyzed.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14502995 (1543-5008 (Print) Journal Article Review14502995tDepartment of Genetics, North Carolina State University, Raleigh, North Carolina 27695, USA. saflintg@unity.ncsu.edu"~?Forrest, W. F.20012Weighting improves the "new Haseman-Elston" method47-54 Hum Hered521*Algorithms Chromosome Mapping Chromosomes, Human/genetics Computer Simulation Data Interpretation, Statistical *Genetic Techniques Humans Linkage (Genetics)/*genetics Quantitative Trait, Heritable *Regression AnalysisElston et al. [Genet Epidemiol, in press] apply the results of Wright [Am J Hum Genet 1997;60:740-742] and Drigalenko [Am J Hum Genet 1998;63:1242-1245] to extend the traditional Haseman-Elston regression scheme [Haseman and Elston, Behav Genet 1972;2:3-19] to include not only linkage information contained in the sib pair's squared difference, but also information in their mean-corrected squared sum. The new algorithm detects linkage to a quantitative trait locus by modelling sib pair trait covariance as a function of identity-by-descent status. We demonstrate why this new estimator is suboptimal and can in some cases be inferior to the original Haseman-Elston method. We also describe a simple approach to estimation which improves on this new Haseman-Elston method by incorporating variance-based weights into the test statistic while staying within the linear modelling framework. In support of our theoretical claim, we conduct both a sib pair simulation and an application to GAW 10 sib pair data showing that our new estimator is superior to both the old and new Haseman-Elston schemes currently implemented in the analysis package S.A.G.E. 4.0.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11359067 F0001-5652 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11359067Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, PA 15261, USA. forrest@forrest.hgen.pitt.edu /~? Forrest, W. F. Feingold, E.2000MComposite statistics for QTL mapping with moderately discordant sibling pairs1642-60Am J Hum Genet665>Chromosome Mapping/*methods/*statistics & numerical data Computer Simulation Crosses, Genetic Female Gene Frequency/genetics Genotype Humans Likelihood Functions Lod Score Male Matched-Pair Analysis Models, Genetic *Nuclear Family *Quantitative Trait, Heritable Regression Analysis Research Design Sample Size SoftwareMayExtreme discordant sibling-pair (EDSP) designs have been shown in theory to be very powerful for mapping quantitative-trait loci (QTLs) in humans. However, their practical applicability has been somewhat limited by the need to phenotype very large populations to find enough pairs that are extremely discordant. In this paper, we demonstrate that there is also substantial power in pairs that are only moderately discordant, and that designs using moderately discordant pairs can yield a more practical balance between phenotyping and genotyping efforts. The power we demonstrate for moderately discordant pairs stems from a new statistical result. Statistical analysis in discordant-pair studies is generally done by testing for reduced identity by descent (IBD) sharing in the pairs. By contrast, the most commonly-used statistical methods for more standard QTL mapping are Haseman-Elston regression and variance-components analysis. Both of these use statistics that are functions of the trait values given IBD information for the pedigree. We show that IBD sharing statistics and "trait value given IBD" statistics contribute complementary rather than redundant information, and thus that statistics of the two types can be combined to form more powerful tests of linkage. We propose a simple composite statistic, and test it with simulation studies. The simulation results show that our composite statistic increases power only minimally for extremely discordant pairs. However, it boosts the power of moderately discordant pairs substantially and makes them a very practical alternative. Our composite statistic is straightforward to calculate with existing software; we give a practical example of its use by applying it to a Genetic Analysis Workshop (GAW) data set.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10762549 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10762549Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA. forrest@forrest.hgen.pitt.edu.~? `Francks, C. DeLisi, L. E. Shaw, S. H. Fisher, S. E. Richardson, A. J. Stein, J. F. Monaco, A. P.2003^Parent-of-origin effects on handedness and schizophrenia susceptibility on chromosome 2p12-q113225-30 Hum Mol Genet1224Chromosome Mapping *Chromosomes, Human, Pair 2 Female Functional Laterality/*genetics *Genetic Predisposition to Disease Genetic Screening/methods Humans Lod Score Male Schizophrenia/*geneticsDec 15Schizophrenia and non-right-handedness are moderately associated, and both traits are often accompanied by abnormalities of asymmetrical brain morphology or function. We have found linkage previously of chromosome 2p12-q11 to a quantitative measure of handedness, and we have also found linkage of schizophrenia/schizoaffective disorder to this same chromosomal region in a separate study. Now, we have found that in one of our samples (191 reading-disabled sibling pairs), the relative hand skill of siblings was correlated more strongly with paternal than maternal relative hand skill. This led us to re-analyse 2p12-q11 under parent-of-origin linkage models. We found linkage of relative hand skill in the RD siblings to 2p12-q11 with P=0.0000037 for paternal identity-by-descent sharing, whereas the maternally inherited locus was not linked to the trait (P>0.2). Similarly, in affected-sib-pair analysis of our schizophrenia dataset (241 sibling pairs), we found linkage to schizophrenia for paternal sharing with LOD=4.72, P=0.0000016, within 3 cM of the peak linkage to relative hand skill. Maternal linkage across the region was weak or non-significant. These similar paternal-specific linkages suggest that the causative genetic effects on 2p12-q11 are related. The linkages may be due to a single maternally imprinted influence on lateralized brain development that contains common functional polymorphisms.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14583442 g0964-6906 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.14583442_Wellcome Trust Centre for Human Genetics, University of Oxford, UK. clyde.francks@well.ox.ac.uk~? Freedman, M. L. Reich, D. Penney, K. L. McDonald, G. J. Mignault, A. A. Patterson, N. Gabriel, S. B. Topol, E. J. Smoller, J. W. Pato, C. N. Pato, M. T. Petryshen, T. L. Kolonel, L. N. Lander, E. S. Sklar, P. Henderson, B. Hirschhorn, J. N. Altshuler, D.2004PAssessing the impact of population stratification on genetic association studies388-93 Nat Genet364sCase-Control Studies Cohort Studies *Genetics, Population Humans Mutation, Missense Polymorphism, Single NucleotideAprPopulation stratification refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry rather than association of genes with disease. It has been proposed that false positive associations due to stratification can be controlled by genotyping a few dozen unlinked genetic markers. To assess stratification empirically, we analyzed data from 11 case-control and case-cohort association studies. We did not detect statistically significant evidence for stratification but did observe that assessments based on a few dozen markers lack power to rule out moderate levels of stratification that could cause false positive associations in studies designed to detect modest genetic risk factors. After increasing the number of markers and samples in a case-cohort study (the design most immune to stratification), we found that stratification was in fact present. Our results suggest that modest amounts of stratification can exist even in well designed studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15052270 1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.15052270Department of Medicine and Molecular Biology, Massachusetts General Hospital, Boston, and Program in Medical and Population Genetics, Broad Institute, Cambridge, USA.~?  Fulker, D. W.1978;Multivariate extensions of a biometrical model of twin data217-36Prog Clin Biol Res24AAnalysis of Variance Aptitude *Biometry Female Genetics Genetics, Population Humans Intelligence Personality Phenotype Pregnancy *Research Design Social Environment *Twins *Variation (Genetics)dhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=568277 !0361-7742 (Print) Journal Article568277? "Fulker, D.W. Baker, L.A. Bock, R.D19830Estimating components of covariance using Lisrel5-8 Data Analyst 1@~?Fulker, D. W. Cardon, L. R.1994BA sib-pair approach to interval mapping of quantitative trait loci1092-103Am J Hum Genet546Alleles Chromosome Mapping/*methods/statistics & numerical data Chromosomes, Human Genetic Markers Genotype Humans *Linkage (Genetics) *Models, Genetic *Models, StatisticalJunAn interval mapping procedure based on the sib-pair method of Haseman and Elston is developed, and simulation studies are carried out to explore its properties. The procedure is analogous to other interval mapping procedures used with experimental material, such as plants and animals, and yields very similar results in terms of the location and effect size of a quantitative trait locus (QTL). The procedure offers an advantage over the conventional Haseman and Elston approach, in terms of power, and provides useful information concerning the location of a QTL. Because of its simplicity, the method readily lends itself to the analysis of selected samples for increased power and the evaluation of multilocus models of complex phenotypes.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8198132 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8198132IInstitute for Behavioral Genetics, University of Colorado, Boulder 80309.G~?Fulker, D. W. Cherny, S. S.1996?An improved multipoint sib-pair analysis of quantitative traits527-32 Behav Genet265nAnimals Genetic Markers/*genetics *Genotype Humans Likelihood Functions *Models, Genetic Phenotype ProbabilitySepKruglyak and Lander (1995) recently published a multipoint sib-pair procedure based on the expected distribution of zero, one and two marker alleles shared identical by descent (ibd) and the method of maximum-likelihood (ML). Their approach uses phenotypic sib-pair differences, which ignores the bivariate structure of sib-pair data. Their simulations suggested that their method was more powerful than the regression method of Haseman and Elston (1972). We show through computation and simulation that their approach can be made more powerful still if the bivariate nature of sib-pair data is acknowledged. In addition, methods based on the average number of shared alleles that also employ bivariate ML procedures (Nance and Neale, 1989; Xu and Atchley, 1995) are more powerful than the approach they recommend and very similar to true ML using the distribution of ibd. The simple ML approach using the average number of shared alleles that we recommend seems to offer both optimal power and flexibility.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8917952 g0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.8917952mInstitute for Behavioral Genetics, University of Colorado, Boulder 80309-0447, USA. David.Fulker@colorado.edu~?)Fulker, D. W. Cherny, S. S. Cardon, L. R.1995GMultipoint interval mapping of quantitative trait loci, using sib pairs1224-33Am J Hum Genet565Alleles Chromosome Mapping/*methods *Computer Simulation Genetic Markers Humans *Models, Genetic *Nuclear Family Statistics/methods Time FactorsMayThe sib-pair interval-mapping procedure of Fulker and Cardon is extended to take account of all available marker information on a chromosome simultaneously. The method provides a computationally fast multipoint analysis of sib-pair data, using a modified Haseman-Elston approach. It gives results very similar to those of the earlier interval-mapping procedure when marker information is relatively uniform and a coarse map is used. However, there is a substantial improvement over the original method when markers differ in information content and/or when a dense map is employed. The method is illustrated by using simulated sib-pair data.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7726180 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.7726180SInstitute for Behavioral Genetics, University of Colorado, Boulder 80309-0447, USA.~?5Fulker, D. W. Cherny, S. S. Sham, P. C. Hewitt, J. K.1999JCombined linkage and association sib-pair analysis for quantitative traits259-67Am J Hum Genet641aGenotype Humans *Linkage (Genetics) Models, Genetic Nuclear Family *Quantitative Trait, HeritableJanAn extension to current maximum-likelihood variance-components procedures for mapping quantitative-trait loci in sib pairs that allows a simultaneous test of allelic association is proposed. The method involves modeling of the allelic means for a test of association, with simultaneous modeling of the sib-pair covariance structure for a test of linkage. By partitioning of the mean effect of a locus into between- and within-sibship components, the method controls for spurious associations due to population stratification and admixture. The power and efficacy of the method are illustrated through simulation of various models of both real and spurious association.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9915965 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.9915965WInstitute for Behavioral Genetics, University of Colorado, Boulder, CO 80309-0447, USA. 6~?Fullerton, S. M. Clark, A. G. Weiss, K. M. Nickerson, D. A. Taylor, S. L. Stengard, J. H. Salomaa, V. Vartiainen, E. Perola, M. Boerwinkle, E. Sing, C. F.2000Apolipoprotein E variation at the sequence haplotype level: implications for the origin and maintenance of a major human polymorphism881-900Am J Hum Genet674Alleles Alzheimer Disease/genetics Apolipoproteins E/*genetics Base Sequence Cardiovascular Diseases/genetics Ethnic Groups/genetics Evolution, Molecular Finland Gene Frequency Germ Cells/metabolism Haplotypes/*genetics Heterozygote Humans Linkage Disequilibrium Mexico Missouri New York Nucleotides/genetics Polymorphism, Genetic/*genetics Polymorphism, Single Nucleotide/genetics Protein Isoforms/genetics Regulatory Sequences, Nucleic Acid/genetics Time Factors Variation (Genetics)/*geneticsOctoThree common protein isoforms of apolipoprotein E (apoE), encoded by the epsilon2, epsilon3, and epsilon4 alleles of the APOE gene, differ in their association with cardiovascular and Alzheimer's disease risk. To gain a better understanding of the genetic variation underlying this important polymorphism, we identified sequence haplotype variation in 5.5 kb of genomic DNA encompassing the whole of the APOE locus and adjoining flanking regions in 96 individuals from four populations: blacks from Jackson, MS (n=48 chromosomes), Mayans from Campeche, Mexico (n=48), Finns from North Karelia, Finland (n=48), and non-Hispanic whites from Rochester, MN (n=48). In the region sequenced, 23 sites varied (21 single nucleotide polymorphisms, or SNPs, 1 diallelic indel, and 1 multiallelic indel). The 22 diallelic sites defined 31 distinct haplotypes in the sample. The estimate of nucleotide diversity (site-specific heterozygosity) for the locus was 0.0005+/-0.0003. Sequence analysis of the chimpanzee APOE gene showed that it was most closely related to human epsilon4-type haplotypes, differing from the human consensus sequence at 67 synonymous (54 substitutions and 13 indels) and 9 nonsynonymous fixed positions. The evolutionary history of allelic divergence within humans was inferred from the pattern of haplotype relationships. This analysis suggests that haplotypes defining the epsilon3 and epsilon2 alleles are derived from the ancestral epsilon4s and that the epsilon3 group of haplotypes have increased in frequency, relative to epsilon4s, in the past 200,000 years. Substantial heterogeneity exists within all three classes of sequence haplotypes, and there are important interpopulation differences in the sequence variation underlying the protein isoforms that may be relevant to interpreting conflicting reports of phenotypic associations with variation in the common protein isoforms.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10986041 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10986041Institute of Molecular Evolutionary Genetics, Department of Biology, and Department of Anthropology, Pennsylvania State University, University Park, PA 16802, USA. smf15@psu.edu~?Gabriel, S. B. Schaffner, S. F. Nguyen, H. Moore, J. M. Roy, J. Blumenstiel, B. Higgins, J. DeFelice, M. Lochner, A. Faggart, M. Liu-Cordero, S. N. Rotimi, C. Adeyemo, A. Cooper, R. Ward, R. Lander, E. S. Daly, M. J. Altshuler, D.20025The structure of haplotype blocks in the human genome2225-9Science2965576Africa African Americans African Continental Ancestry Group/genetics Alleles Asian Continental Ancestry Group/genetics China Chromosome Mapping Computational Biology Computer Simulation Europe European Continental Ancestry Group/genetics *Genome, Human Genotype *Haplotypes Humans Japan Linkage Disequilibrium Models, Genetic *Polymorphism, Single Nucleotide Recombination, Genetic Variation (Genetics)Jun 21Haplotype-based methods offer a powerful approach to disease gene mapping, based on the association between causal mutations and the ancestral haplotypes on which they arose. As part of The SNP Consortium Allele Frequency Projects, we characterized haplotype patterns across 51 autosomal regions (spanning 13 megabases of the human genome) in samples from Africa, Europe, and Asia. We show that the human genome can be parsed objectively into haplotype blocks: sizable regions over which there is little evidence for historical recombination and within which only a few common haplotypes are observed. The boundaries of blocks and specific haplotypes they contain are highly correlated across populations. We demonstrate that such haplotype frameworks provide substantial statistical power in association studies of common genetic variation across each region. Our results provide a foundation for the construction of a haplotype map of the human genome, facilitating comprehensive genetic association studies of human disease.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12029063 G1095-9203 (Electronic) Journal Article Research Support, Non-U.S. Gov't12029063CWhitehead/MIT Center for Genome Research, Cambridge, MA 02139, USA.L? Galton, F1889Natural Inheritance.London Macmillan T~?0Gauderman, W. J. Morrison, J. L. Siegmund, K. D.2001ZShould we consider gene x environment interaction in the hunt for quantitative trait loci?S831-6Genet Epidemiol 21 Suppl 1Chromosome Mapping/*statistics & numerical data Environmental Exposure/*adverse effects Genetic Markers/genetics Genetic Predisposition to Disease/*genetics *Genotype Humans Lod Score *Models, Genetic Models, Statistical Phenotype *Quantitative Trait, Heritable Statistics, Nonparametric$We address the question of whether one can obtain increased power for finding a quantitative trait locus (QTL) if a gene x environment (G x E) interaction is incorporated directly into the linkage analysis. We consider both parametric and nonparametric analysis approaches to including G x E interaction. For the former, we utilize joint segregation and linkage analysis to estimate simultaneously the recombination fraction and a G x E interaction effect, as well as the remaining model parameters. The nonparametric approach is based on an extension of the Haseman-Elston method applied to sib pairs to include a regression of the squared trait difference on marker-identity-by-descent (IBD) probability (pi), the sibling covariate sum (z), and pi x z. We utilize 50 replicates of the simulated data and compare empirical power of the various approaches to detect MG4, a locus that is involved in a strong interaction with age for Q4 and in a weaker interaction with environmental factor E2 for Q3. Using the parametric approach, including a G x age effect does increase power for detecting linkage between MG4 and Q4 compared with ignoring the interaction (powers 58% and 38%, respectively, to exceed a lod score of 3.0). On the other hand, including a G x E2 interaction has little effect on the power to detect linkage between MG4 and Q3. The nonparametric approach leads to qualitatively similar findings. We conclude that it is beneficial to incorporate G x E interaction into a linkage analysis, provided the interaction effect is of sufficiently strong magnitude.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11793788 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11793788Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, Suite 220, Los Angeles, CA 90033, USA.?.Gelman, A Carlin, J.B. Stern, H.S. Rubin, D.B.2004!Bayesian Data Analysis, 2nd Edn. LondonChapman and Halll?'Gentleman, R. Rossini, A.J. Sudoit, S. 2006 Bioconductorhttp://www.bioconductor.org/ T~?)Ghosh, S. Karanjawala, Z. E. Hauser, E. R. Ally, D. Knapp, J. I. Rayman, J. B. Musick, A. Tannenbaum, J. Te, C. Shapiro, S. Eldridge, W. Musick, T. Martin, C. Smith, J. R. Carpten, J. D. Brownstein, M. J. Powell, J. I. Whiten, R. Chines, P. Nylund, S. J. Magnuson, V. L. Boehnke, M. Collins, F. S.1997Methods for precise sizing, automated binning of alleles, and reduction of error rates in large-scale genotyping using fluorescently labeled dinucleotide markers. FUSION (Finland-U.S. Investigation of NIDDM Genetics) Study Group165-78 Genome Res72f*Alleles Automatic Data Processing/methods Blotting, Southern/*methods Chromosome Mapping/*methods DNA/isolation & purification DNA-Directed DNA Polymerase/genetics *Dinucleotide Repeats Electrophoresis, Polyacrylamide Gel/*methods Genetic Markers Genetic Techniques Genotype Humans Linkage (Genetics) Polymerase Chain Reaction Quality Control Taq PolymeraseFebLarge-scale genotyping is required to generate dense identity-by-descent maps to map genes for human complex disease. In some studies the number of genotypes needed can approach or even exceed 1 million. Generally, linkage and linkage disequilibrium analyses depend on clear allele identification and subsequent allele frequency estimation. Accurate grouping or categorization of each allele in the sample (allele calling or binning) is therefore an absolute requirement. Hence, a genotyping system that can reliably achieve this is necessary. In the case of affected sib-pair analysis without parents, the need for accurate allele calling is even more critical. We describe methods that permit precise sizing of alleles across multiple gels using the fluorescence-based, Applied Biosystems (ABI) genotyping technology and discuss ways to reduce genotyping error rates. Using database utilities, we show how to minimize intergel allele size variation, to combine data effectively from different models of ABI sequencing machines, and automatically bin alleles. The final data can then be converted into a format ready for analysis by statistical genetic packages such as MENDEL.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9049634 !1088-9051 (Print) Journal Article9049634lPositional Cloning Section, National Institutes of Health, Bethesda, Maryland 20892, USA. sghosh@alw.nlh.gov? .Gilks, W.R. Richardson, S. Spiegelhalter, D.J.1996%Markov Chain Monte Carlo in practice.LondonChapman and Hallo?"Gill, P.E. Murray, W. Wright, M.H.1981Practical Optimization.LondonAcademic Press.  ~?Gillespie, N. A. Neale, M. C.2006kA finite mixture model for genotype and environment interactions: detecting latent population heterogeneity412-23Twin Res Hum Genet93Biometry/*methods Environment *Genetics, Population Genotype Humans *Models, Genetic *Models, Statistical Quantitative Trait, Heritable Twin Studies Twins/*genetics *Variation (Genetics)Jun Approaches such as DeFries-Fulker extremes regression (LaBuda et al., 1986) are commonly used in genetically informative studies to assess whether familial resemblance varies as a function of the scores of pairs of twins. While useful for detecting such effects, formal modeling of differences in variance components as a function of pairs' trait scores is rarely attempted. We therefore present a finite mixture model which specifies that the population consists of latent groups which may differ in (i) their means, and (ii) the relative impact of genetic and environmental factors on within-group variation and covariation. This model may be considered as a special case of a factor mixture model, which combines the features of a latent class model with those of a latent trait model. Various models for the class membership of twin pairs may be employed, including additive genetic, common environment, specific environment or major locus (QTL) factors. Simulation results based on variance components derived from Turkheimer and colleagues (2003), illustrate the impact of factors such as the difference in group means and variance components on the feasibility of correctly estimating the parameters of the mixture model. Model-fitting analyses estimated group heritability as .49, which is significantly greater than heritability for the rest of the population in early childhood. These results suggest that factor mixture modeling is sufficiently robust for detecting heterogeneous populations even when group mean differences are modest.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16790151 g1832-4274 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16790151Virginia Institute of Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, 23219, USA. ngillespie@vcu.edu~?*Glaser, R. L. Ramsay, J. P. Morison, I. M.2006iThe imprinted gene and parent-of-origin effect database now includes parental origin of de novo mutationsD29-31Nucleic Acids Res34Database issueuAnimals *Databases, Genetic Genetic Predisposition to Disease *Genomic Imprinting Humans Internet Mice *Mutation RatsJan 1The imprinted gene and parent-of-origin effect database (www.otago.ac.nz/IGC) consists of two sections. One section catalogues the current literature on imprinted genes in humans and animals. The second, and new, section catalogues current reports of parental origin of de novo mutations in humans alone. The addition of a catalogue of de novo mutations that show a parent-of-origin effect expands the scope of the database and provides a useful tool for examining parental origin trends for different types of spontaneous mutations. This new section includes >1700 mutations, found in 59 different disorders. The 85 imprinted genes are described in 152 entries from several mammalian species. In addition, >300 other entries describe a range of reported parent-of-origin effects in animals.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16381868 G1362-4962 (Electronic) Journal Article Research Support, Non-U.S. Gov't16381868jDepartment of Biology, Massachusetts College of Liberal Arts, North Adams, MA 01247, USA. rglaser@mcla.edu ~?'Gordon, D. Heath, S. C. Liu, X. Ott, J.2001{A transmission/disequilibrium test that allows for genotyping errors in the analysis of single-nucleotide polymorphism data371-80Am J Hum Genet692'Alleles Chromosome Mapping/*methods/statistics & numerical data Computer Simulation Gene Frequency Genes, Dominant Genetic Diseases, Inborn/genetics Genotype Humans Linkage Disequilibrium/*genetics Models, Genetic Penetrance Polymorphism, Single Nucleotide/*genetics Probability *Research DesignAugnThe present study assesses the effects of genotyping errors on the type I error rate of a particular transmission/disequilibrium test (TDT(std)), which assumes that data are errorless, and introduces a new transmission/disequilibrium test (TDT(ae)) that allows for random genotyping errors. We evaluate the type I error rate and power of the TDT(ae) under a variety of simulations and perform a power comparison between the TDT(std) and the TDT(ae), for errorless data. Both the TDT(std) and the TDT(ae) statistics are computed as two times a log-likelihood difference, and both are asymptotically distributed as chi(2) with 1 df. Genotype data for trios are simulated under a null hypothesis and under an alternative (power) hypothesis. For each simulation, errors are introduced randomly via a computer algorithm with different probabilities (called "allelic error rates"). The TDT(std) statistic is computed on all trios that show Mendelian consistency, whereas the TDT(ae) statistic is computed on all trios. The results indicate that TDT(std) shows a significant increase in type I error when applied to data in which inconsistent trios are removed. This type I error increases both with an increase in sample size and with an increase in the allelic error rates. TDT(ae) always maintains correct type I error rates for the simulations considered. Factors affecting the power of the TDT(ae) are discussed. Finally, the power of TDT(std) is at least that of TDT(ae) for simulations with errorless data. Because data are rarely error free, we recommend that researchers use methods, such as the TDT(ae), that allow for errors in genotype data.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11443542 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11443542tLaboratory of Statistical Genetics, Rockefeller University, New York, NY, 10021, USA. gordon@linkage.rockefeller.edu~?Gordon, D. Heath, S. C. Ott, J.1999[True pedigree errors more frequent than apparent errors for single nucleotide polymorphisms65-70 Hum Hered492VAlleles Genotype Humans Models, Statistical *Mutation *Pedigree *Polymorphism, GeneticMarSingle nucleotide polymorphisms (SNPs) are currently being developed for use in disequilibrium analyses. These SNPs consist of two alleles with varying degrees of polymorphism. A natural design for use with SNPs is the 'haplotype relative risk' sampling design in which a father, mother, and child are typed at an SNP locus. Given such a trio of genotypes, we ask: what is the probability that a pedigree error (a change from one allele to the other) at an SNP locus will be detected using only Mendel's laws as a check? We calculate the probability of detecting such errors for a hypothetical SNP locus with varying degrees of polymorphism and for various true error rates. For the sets of allele frequencies considered, we find that the detection rates range between 25 and 30%, the detection rate being lowest when the two alleles have equal frequencies and the highest when one allele has a frequency of 10%. Based on this detection rate, we determine that the true error rate is roughly 3.3-4 times that of the apparent error rate at an SNP locus. The greatest discrepancy between true and apparent error rates occurs when allele frequencies are equal.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10077724 J0001-5652 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S.10077724TLaboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA.h~?Goring, H. H. Ott, J.1997LRelationship estimation in affected sib pair analysis of late-onset diseases69-77Eur J Hum Genet52Bayes Theorem Data Interpretation, Statistical Decision Making Factor Analysis, Statistical *Genetic Diseases, Inborn Genetic Markers *Genotype Humans *Linkage (Genetics) *Models, Genetic Models, Statistical *Pedigree SoftwareMar-AprIn linkage studies, errors in pedigree structure will often be uncovered through Mendelian inconsistencies. In affected sib pair analysis of diseases with late onset, however, such mistakes will usually go undetected since parental genotypes are commonly not known. Cases of nonpaternity, unrecorded adoption or accidental sample swap in the laboratory will then not be noticed. Typically, such relationship errors lead to a decrease in power for linkage. In this paper, a method is presented which allows verification of the relationship between stated sibs using their marker genotypes. The method is likelihood-based and incorporates a Bayesian approach to compute posterior relationship probabilities. It is shown that sibs, half-sibs and unrelated individuals can be distinguished from each other quite reliably using numbers of markers that should be available in most sib pair studies. It is demonstrated that elimination of false sib pairs increases the power to detect linkage in affected sib pair studies. The gain in power may be large if relationship errors occur quite frequently; the gain will be only moderate if relationship errors are very infrequent. Software for relationship estimation is provided.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9195155 J1018-4813 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S.9195155WDepartment of Genetics and Development, Columbia University, New York, N.Y. 10032, USA. ~?  Goring, H. H. Terwilliger, J. D.2000`Linkage analysis in the presence of errors III: marker loci and their map as nuisance parameters1298-309Am J Hum Genet664Alleles Chromosome Mapping/*methods/statistics & numerical data Computer Simulation False Positive Reactions Female Gene Frequency/genetics Genetic Diseases, Inborn/*genetics Genetic Markers/*genetics Genotype Haplotypes/genetics Humans Likelihood Functions Linkage (Genetics)/*genetics Lod Score Male Microsatellite Repeats/genetics Models, Genetic Pedigree Polymorphism, Single Nucleotide/genetics Reproducibility of Results *Research Design SoftwareAprJIn linkage and linkage disequilibrium (LD) analysis of complex multifactorial phenotypes, various types of errors can greatly reduce the chance of successful gene localization. The power of such studies-even in the absence of errors-is quite low, and, accordingly, their robustness to errors can be poor, especially in multipoint analysis. For this reason, it is important to deal with the ramifications of errors up front, as part of the analytical strategy. In this study, errors in the characterization of marker-locus parameters-including allele frequencies, haplotype frequencies (i.e., LD between marker loci), recombination fractions, and locus order-are dealt with through the use of profile likelihoods maximized over such nuisance parameters. It is shown that the common practice of assuming fixed, erroneous values for such parameters can reduce the power and/or increase the probability of obtaining false positive results in a study. The effects of errors in assumed parameter values are generally more severe when a larger number of less informative marker loci, like the highly-touted single nucleotide polymorphisms (SNPs), are analyzed jointly than when fewer but more informative marker loci, such as microsatellites, are used. Rather than fixing inaccurate values for these parameters a priori, we propose to treat them as nuisance parameters through the use of profile likelihoods. It is demonstrated that the power of linkage and/or LD analysis can be increased through application of this technique in situations where parameter values cannot be specified with a high degree of certainty.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10731467 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.10731467ODepartment of Genetics and Development, Columbia University, New York, NY, USA. ~?! Goring, H. H. Terwilliger, J. D.2000Linkage analysis in the presence of errors II: marker-locus genotyping errors modeled with hypercomplex recombination fractions1107-18Am J Hum Genet6632Algorithms Chromosome Mapping/*methods/statistics & numerical data Genetic Markers/*genetics Genetic Predisposition to Disease/genetics Genome, Human Genotype Humans Likelihood Functions Lod Score Meiosis/genetics *Models, Genetic Recombination, Genetic/*genetics Reproducibility of Results Research DesignMar|It is well known that genotyping errors lead to loss of power in gene-mapping studies and underestimation of the strength of correlations between trait- and marker-locus genotypes. In two-point linkage analysis, these errors can be absorbed in an inflated recombination-fraction estimate, leaving the test statistic quite robust. In multipoint analysis, however, genotyping errors can easily result in false exclusion of the true location of a disease-predisposing gene. In a companion article, we described a "complex-valued" extension of the recombination fraction to accommodate errors in the assignment of trait-locus genotypes, leading to a multipoint LOD score with the same robustness to errors in trait-locus genotypes that is seen with the conventional two-point LOD score. Here, a further extension of this model to "hypercomplex-valued" recombination fractions (hereafter referred to as "hypercomplex recombination fractions") is presented, to handle random and systematic sources of marker-locus genotyping errors. This leads to a multipoint method (either "model-based" or "model-free") with the same robustness to marker-locus genotyping errors that is seen with conventional two-point analysis but with the advantage that multiple marker loci can be used jointly to increase meiotic informativeness. The cost of this increased robustness is a decrease in fine-scale resolution of the estimated map location of the trait locus, in comparison with traditional multipoint analysis. This probability model further leads to algorithms for the estimation of the lower bounds for the error rates for genomewide and locus-specific genotyping, based on the null-hypothesis distribution of the LOD-score statistic in the presence of such errors. It is argued that those genome scans in which the LOD score is 0 for >50% of the genome are likely to be characterized by high rates of genotyping errors in general.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10712221 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.10712221ODepartment of Genetics and Development, Columbia University, New York, NY, USA.~?"MGorlova, O. Y. Amos, C. I. Wang, N. W. Shete, S. Turner, S. T. Boerwinkle, E.2003VGenetic linkage and imprinting effects on body mass index in children and young adults425-32Eur J Hum Genet116Adolescent Adult Age Factors *Body Mass Index Child Child, Preschool Chromosomes, Human, Pair 16/genetics/physiology Chromosomes, Human, Pair 20/genetics/physiology Genomic Imprinting/*genetics Humans Linkage (Genetics)/*genetics/*physiology *Models, GeneticJunBody mass index (BMI) is used as a measure of fatness. Here we performed a genome-wide scan for genes related to BMI, while allowing for the possible effects of imprinting. We applied a sib pair linkage analysis to a sample of primarily children and young adults by using the Haseman-Elston method, which we modified to model the separate effects of paternally and maternally derived genetic factors. After stratification of sib pairs according to age, a number of regions showing linkage with BMI were identified. Most linkage and imprinting effects were found in children 5-11 years of age. Strongest evidences for linkage in children were found on chromosome 20 at 20p11.2-pter near the marker D20S851 (LOD(Total)=4.08, P=0.000046) and near the marker D20S482 (LOD(Total) =3.55, P=0.00016), and Chromosome 16 at 16p13 near the marker ATA41E04 (LOD(Total) =3.12, P=0.00025), and those loci did not show significant evidence for imprinting. Six regions showing evidence of imprinting were 3p23-p24 (paternal expression), 4q31.1-q32 (maternal expression), 10p14-q11 (paternal expression), and 12p12-pter (paternal expression) in children, and 4q31-qter (paternal expression) and 8p (paternal expression) in adults.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12774034 X1018-4813 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.12774034fDepartment of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.<~?#cGosso, M. F. van Belzen, M. de Geus, E. J. Polderman, J. C. Heutink, P. Boomsma, D. I. Posthuma, D.2006UAssociation between the CHRM2 gene and intelligence in a sample of 304 Dutch families577-84Genes Brain Behav58Adolescent Adult Child Chromosomes, Human, Pair 7/genetics Female Genetics, Population Humans Intelligence/*genetics Male Middle Aged Netherlands Pedigree Polymorphism, Single Nucleotide Receptor, Muscarinic M2/*genetics Twins, Dizygotic Twins, Monozygotic *Variation (Genetics)NovThe CHRM2 gene is thought to be involved in neuronal excitability, synaptic plasticity and feedback regulation of acetylcholine release and has previously been implicated in higher cognitive processing. In a sample of 667 individuals from 304 families, we genotyped three single-nucleotide polymorphisms (SNPs) in the CHRM2 gene on 7q31-35. From all individuals, standardized intelligence measures were available. Using a test of within-family association, which controls for the possible effects of population stratification, a highly significant association was found between the CHRM2 gene and intelligence. The strongest association was between rs324650 and performance IQ (PIQ), where the T allele was associated with an increase of 4.6 PIQ points. In parallel with a large family-based association, we observed an attenuated - although still significant - population-based association, illustrating that population stratification may decrease our chances of detecting allele-trait associations. Such a mechanism has been predicted earlier, and this article is one of the first to empirically show that family-based association methods are not only needed to guard against false positives, but are also invaluable in guarding against false negatives.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17081262 _1601-1848 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Twin Study17081262eDepartment of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands. mf.gosso@vumc.nl~?$Greenberg, D. A.19899Inferring mode of inheritance by comparison of lod scores480-6Am J Med Genet344xFemale *Genes, Dominant *Genes, Recessive Genotype Humans *Linkage (Genetics) *Lod Score Male *Models, Genetic PhenotypeDeclOne usually must assume a mode of inheritance when using lod scores for linkage analysis. In this study, we asked the question, "If one assumed mode of inheritance in a linkage analysis gives a higher lod score than another, does that indicate that the mode of inheritance that led to the higher lod score is more 'correct' than the other?" We simulated data under a variety of penetrances, assuming either dominant or recessive inheritance. We then analyzed those simulated data under the correct mode of inheritance, assuming a range of penetrance values, and under the incorrect model, also assuming a range of penetrance values. We found that, if there was enough information for a maximum lod score of at least 3.0, assuming the correct penetrance value or mode of inheritance in the analysis led to a higher lod score than assuming the incorrect penetrance or the incorrect mode of inheritance. These results cannot yet be generalized outside of the specific modes of inheritance and penetrance combinations that we have modeled. Also, penetrance was modeled as "random." The effect of "reduced penetrance" caused by other genetic factors has not yet been tested. We also tested the effect of non-standard ascertainment on drawing conclusions about mode of inheritance from linkage data. Even when families were ascertained only if the family was multiplex (i.e., more than one affected sib), assuming the correct mode of inheritance gave a higher lod score than assuming the incorrect mode of inheritance. This method has the promise of both simplifying and expanding the application of linkage analysis.(ABSTRACT TRUNCATED AT 250 WORDS)ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2624256 o0148-7299 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.2624256IDepartment of Psychiatry, Mount Sinai Medical Center, New York, NY 10029.`?% Greene, W.H.1993 Econometric Analysis, 2nd Edn New York, NY. Macmillan~?&Gu, C. Todorov, A. Rao, D. C.1996Combining extremely concordant sibpairs with extremely discordant sibpairs provides a cost effective way to linkage analysis of quantitative trait loci513-33Genet Epidemiol136Costs and Cost Analysis Epidemiologic Methods Genetic Techniques/economics Genotype Humans *Linkage (Genetics) *Models, Genetic *Models, Statistical Nuclear Family Sample Size?Extremely discordant (ED) sibpairs have been shown to be very powerful for linkage analysis of human quantitative traits [Risch and Zhang (1995) Science 268: 1584-1589]. In many cases, the extremely concordant (EC) sibpairs collected in the process of screening for ED sibpairs carry valuable information for linkage. Therefore, it seems justifiable to investigate the advantages of genotyping and to include them with the ED sibpairs for linkage analysis. Herein we explore the distributions of EC as well as ED sibpairs under various genetic models and provide a basis for combining both types of sibpairs. A simple statistic testing means of genes shared identical by descent (IBD) in applied to combine both types of EC sibpairs (high-high and low-low) with the ED pairs. We show that when a decent number of EC pairs is added to the ED sample for analysis, the power is much enhanced, making it especially desirable when the number of available ED pairs is small. We show at the same time that combining EC pairs with ED pairs is more cost effective than pursuing ED sibpairs alone.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8968712 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8968712^Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, USA.~?'7Gudbjartsson, D. F. Jonasson, K. Frigge, M. L. Kong, A.2000?Allegro, a new computer program for multipoint linkage analysis12-3 Nat Genet251zAlgorithms Female Genetic Markers Humans *Linkage (Genetics) Lod Score Male Pedigree *Software/statistics & numerical dataMayfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10802644 1061-4036 (Print) Letter10802644~?( Guo, S. W.1997DLinkage disequilibrium measures for fine-scale mapping: a comparison301-14 Hum Hered476[Alleles *Chromosome Mapping Gene Frequency Genetic Markers *Linkage Disequilibrium MutationNov-DechWe investigate properties of simple linkage disequilibrium mapping for five measures in the presence of mutation at the marker and/or the disease locus and of initial incomplete linkage disequilibrium. In contrast to the stimulation approach that Devlin and Risch used, we calculate the expected values of various linkage disequilibrium measures under different assumptions based on a framework for linkage disequilibrium mapping. These expected values clearly demonstrate the expected performance of these measures. We find that the impact of marker mutation on their performance depends on the magnitude of the mutation relative to the proximity of the marker (i.e. recombination fraction between the marker and the disease locus). In the presence of recurrent mutation at the marker and/or disease locus, the performance of all measures, including the robust one, depends on the marker allele frequency. The initial incomplete linkage disequilibrium could render all measures useless. These expected values also show clearly why in Devlin and Risch's simulation some measures performed very badly under certain circumstances.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9391823 _0001-5652 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S. Review9391823aDivision of Epidemiology, University of Minnesota, Minneapolis 55454-1015, USA. swguo@med.umn.edu*~?)Guo, S. W. Thompson, E. A.1992KPerforming the exact test of Hardy-Weinberg proportion for multiple alleles361-72 Biometrics482g*Alleles Gene Frequency *Genetics, Population Mathematics *Models, Genetic Monte Carlo Method PhenotypeJunThe Hardy-Weinberg law plays an important role in the field of population genetics and often serves as a basis for genetic inference. Because of its importance, much attention has been devoted to tests of Hardy-Weinberg proportions (HWP) over the decades. It has long been recognized that large-sample goodness-of-fit tests can sometimes lead to spurious results when the sample size and/or some genotypic frequencies are small. Although a complete enumeration algorithm for the exact test has been proposed, it is not of practical use for loci with more than a few alleles due to the amount of computation required. We propose two algorithms to estimate the significance level for a test of HWP. The algorithms are easily applicable to loci with multiple alleles. Both are remarkably simple and computationally fast. Relative efficiency and merits of the two algorithms are compared. Guidelines regarding their usage are given. Numerical examples are given to illustrate the practicality of the algorithms.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1637966 o0006-341X (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.1637966EDepartment of Biostatistics, University of Washington, Seattle 98195.~?*Guo, X. Elston, R. C.1999:Linkage information content of polymorphic genetic markers112-8 Hum Hered492nGene Frequency *Genetic Markers Humans *Linkage (Genetics) Models, Statistical Pedigree *Polymorphism, GeneticMarThe Polymorphism Information Content (PIC) value is often used to measure the informativeness of a genetic marker for linkage studies. The PIC value was first derived for the case of a rare dominant disease, when one of the parents is affected, and is a function of the particular mode of disease inheritance. We first generalize the definition of the PIC value in such a way that it does not depend on the mode of inheritance of the trait being studied, and then develop a Linkage Information Content (LIC) value to measure the informativeness of a marker about the identity-by-descent sharing status of particular types of pairs of relatives. Knowing the LIC value, it is possible to determine the effective number of fully informative pairs in a study when we have incomplete marker information.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10077733 F0001-5652 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10077733Department of Epidemiology and Biostatistics, Rammelkamp Center for Education and Research, MetroHealth Campus, Case Western Reserve University, Cleveland, Ohio 44109-1998, USA. r~?+XGusella, J. F. Keys, C. VarsanyiBreiner, A. Kao, F. T. Jones, C. Puck, T. T. Housman, D.1980JIsolation and localization of DNA segments from specific human chromosomes2829-33Proc Natl Acad Sci U S A775Animals Cells, Cultured Chromosome Mapping *Chromosomes, Human, 6-12 and X Cloning, Molecular/*methods Cricetinae DNA/genetics/*isolation & purification DNA, Recombinant Humans Hybrid Cells Nucleic Acid HybridizationMayRecombinant DNA techniques have been combined with somatic cell genetic methods to identify, isolate, and amplify fragments of human DNA localized at specific regions of human chromosome 11 selected as a model system. A library of genomic DNA segments has been constructed, in lambda Charon 4A bacteriophage, from the DNA of a somatic cell hybrid carrying a portion of human chromosome 11 on a Chinese hamster ovary cell background. Using a nucleic acid hybridization technique that distinguishes human and Chinese hamster interspersed, repetitive DNA, we have been able to distinguish recombinant phages carrying DNA segments of human origin from recombinant phages carrying DNA segments of Chinese hamster origin. We have isolated 50 human DNA segments thus far and have characterized 5 in detail. For each DNA segment characterized, a subsegment that carries no repetitive human DNA sequences has been identified. These segments have been used as hybridization probes in experiments that localize the DNA fragment on the chromosome. In each case an unequivocal chromosomal localization has been obtained with reference to a panel of hybrid cell clones each of which carries a deletion of a portion of the short arm of chromosome 11. At least one DNA segment has been identified which maps to each of the four regions on the short arm defined by the panel of hybrid cell clones used. The approaches described here appear to be general. They can be extended to produce a fine structure map of human chromosome 11 and other human chromosomes. This approach promises implications for human genetics generally, for the human genetic diseases, and possibly for understanding of gene regulation in normal and abnormal differentiation.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6930670 o0027-8424 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.6930670~?,Gusella, J. F. Wexler, N. S. Conneally, P. M. Naylor, S. L. Anderson, M. A. Tanzi, R. E. Watkins, P. C. Ottina, K. Wallace, M. R. Sakaguchi, A. Y. et al.,1983CA polymorphic DNA marker genetically linked to Huntington's disease234-8Nature3065940Chromosome Mapping *Chromosomes, Human, 4-5 Cloning, Molecular DNA Restriction Enzymes/diagnostic use Female Humans Huntington Disease/diagnosis/*genetics Linkage (Genetics) Male Pedigree Polymorphism, Genetic Nov 17-23*Family studies show that the Huntington's disease gene is linked to a polymorphic DNA marker that maps to human chromosome 4. The chromosomal localization of the Huntington's disease gene is the first step in using recombinant DNA technology to identify the primary genetic defect in this disorder.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6316146 g0028-0836 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.6316146~?-Halpern, J. Whittemore, A. S.1999.Multipoint linkage analysis. A cautionary note194-6 Hum Hered494Computer Simulation *Genetic Markers Humans *Linkage (Genetics) Male Models, Genetic Models, Statistical Polymorphism, Genetic Prostatic Neoplasms/geneticsJullMultipoint linkage analysis is commonly used to evaluate linkage of a disease to multiple markers in a small region. Multipoint analysis is particularly powerful when the IBD relations of family members at the trait locus are ambiguous. The increased power arises because, unlike single-marker analyses, multipoint analysis uses haplotype information from several markers to infer the IBD relations. We wish to temper this advantage with a cautionary note: multipoint analysis is sensitive to power loss due to misspecification of intermarker distances. Such misspecification is especially problematic when dealing with closely spaced markers. We present computer simulations comparing the power of single-point and multipoint analyses, both when IBD relations are ambiguous, and when the intermarker distances are misspecified. We conclude that when evaluating markers in a small region to confirm or refute previous findings, a situation in which p values of modest statistical significance are important, single marker analyses may provide more reliable measures of the strength of support for linkage than multipoint statistics.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10436380 F0001-5652 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10436380oDepartment of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305-5405, USA. ~?.5Hanson, R. L. Kobes, S. Lindsay, R. S. Knowler, W. C.2001QAssessment of parent-of-origin effects in linkage analysis of quantitative traits951-62Am J Hum Genet6846Alleles Chromosome Mapping/*methods/statistics & numerical data Computer Simulation Female Genetic Markers/genetics Genetic Predisposition to Disease/genetics Genomic Imprinting/*genetics Humans Lod Score Male Models, Genetic *Parents Pedigree *Quantitative Trait, Heritable Regression Analysis Research DesignAprKMethods are presented for incorporation of parent-of-origin effects into linkage analysis of quantitative traits. The estimated proportion of marker alleles shared identical by descent is first partitioned into a component derived from the mother and a component derived from the father. These parent-specific estimates of allele sharing are used in variance-components or Haseman-Elston methods of linkage analysis so that the effect of the quantitative-trait locus carried on the maternally derived chromosome is potentially different from the effect of the locus on the paternally derived chromosome. Statistics for linkage between trait and marker loci derived from either or both parents are then calculated, as are statistics for testing whether the effect of the maternally derived locus is equal to that of the paternally derived locus. Analyses of data simulated for 956 siblings from 263 nuclear families who had participated in a linkage study revealed that type I error rates for these statistics were generally similar to nominal values. Power to detect an imprinted locus was substantially increased when analyzed with a model allowing for parent-of-origin effects, compared with analyses that assumed equal effects; for example, for an imprinted locus accounting for 30% of the phenotypic variance, the expected LOD score was 4.5 when parent-of-origin effects were incorporated into the analysis, compared with 3.1 when these effects were ignored. The ability to include parent-of-origin effects within linkage analysis of quantitative traits will facilitate genetic dissection of complex traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11254452 !0002-9297 (Print) Journal Article11254452Diabetes and Arthritis Epidemiology Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ 85014, USA. rhanson@phx.niddk.nih.gov~?/ Hardy, G. H.1908+Mendelian Proportions in a Mixed Population49-50Science28706Jul 10fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17779291 &1095-9203 (Electronic) Journal article17779291n?0Hartl, D.L. Clark, A.G1997"Principles of Population Genetics.Sunderland, MA.Sinauer~?1Haseman, J. K. Elston, R. C.1972LThe investigation of linkage between a quantitative trait and a marker locus3-19 Behav Genet21Alleles Female *Genes Genetics, Behavioral Genetics, Medical Genotype Humans *Linkage (Genetics) Male Models, Biological Phenotype ProbabilityMarehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=4157472 !0001-8244 (Print) Journal Article4157472 ~?2SHauser, E. R. Watanabe, R. M. Duren, W. L. Bass, M. P. Langefeld, C. D. Boehnke, M.2004DOrdered subset analysis in genetic linkage mapping of complex traits53-63Genet Epidemiol2719Breast Neoplasms/genetics Chromosome Mapping/*methods Computer Simulation Confounding Factors (Epidemiology) Female *Genetic Heterogeneity Genetic Predisposition to Disease/*genetics Humans Linkage (Genetics)/*genetics Lod Score Male *Models, Genetic Models, Statistical Nuclear Family Sensitivity and SpecificityJul?Etiologic heterogeneity is a fundamental feature of complex disease etiology; genetic linkage analysis methods to map genes for complex traits that acknowledge the presence of genetic heterogeneity are likely to have greater power to identify subtle changes in complex biologic systems. We investigate the use of trait-related covariates to examine evidence for linkage in the presence of heterogeneity. Ordered-subset analysis (OSA) identifies subsets of families defined by the level of a trait-related covariate that provide maximal evidence for linkage, without requiring a priori specification of the subset. We propose that examining evidence for linkage in the subset directly may result in a more etiologically homogeneous sample. In turn, the reduced impact of heterogeneity will result in increased overall evidence for linkage to a specific region and a more distinct lod score peak. In addition, identification of a subset defined by a specific trait-related covariate showing increased evidence for linkage may help refine the list of candidate genes in a given region and suggest a useful sample in which to begin searching for trait-associated polymorphisms. This method provides a means to begin to bridge the gap between initial identification of linkage and identification of the disease predisposing variant(s) within a region when mapping genes for complex diseases. We illustrate this method by analyzing data on breast cancer age of onset and chromosome 17q [Hall et al., 1990, Science 250:1684-1689]. We evaluate OSA using simulation studies under a variety of genetic models.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15185403 g0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15185403Section of Medical Genetics, Department of Medicine, Center for Human Genetics, Duke University Medical Center, Durham, North Carolina 27710, USA. Elizabeth.Hauser@duke.edua~?38Haviland, M. B. Kessling, A. M. Davignon, J. Sing, C. F.1995}Cladistic analysis of the apolipoprotein AI-CIII-AIV gene cluster using a healthy French Canadian sample. I. Haploid analysis211-31 Ann Hum Genet59Pt 2AAdult Apolipoprotein A-I/*genetics Apolipoprotein C-III Apolipoproteins A/genetics Apolipoproteins C/genetics Canada Chromosome Mapping Female France/ethnology Haplotypes Humans Male Middle Aged Multigene Family/*genetics Polymorphism, Restriction Fragment Length *Repetitive Sequences, Nucleic Acid *Variation (Genetics)AprA cladistic analysis was carried out to identify haplotypes hypothesized to differ for functional DNA sequence variations within the apolipoprotein (apo) AI-CIII-AIV gene cluster that affect plasma lipid, lipoprotein and apolipoprotein levels. A sample of unrelated healthy French Canadians was studied. First, a cladogram of the observed apo AI-CIII-AIV haplotypes was estimated. Then this cladogram was used to define a statistical analysis of the association between haplotype variation and variation in plasma lipid, lipoprotein and apolipoprotein levels. Three haplotypes were identified which were associated with small (5-12% of the total sum of squares) pleiotropic effects on plasma lipid, lipoprotein and apolipoprotein traits and these effects were context, i.e. gender, dependent.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7625767 B0003-4800 (Print) Journal Article Research Support, Non-U.S. Gov't7625767PDepartment of Human Genetics, University of Michigan, Ann Arbor 48109-0618, USA.~?4GHavill, L. M. Dyer, T. D. Richardson, D. K. Mahaney, M. C. Blangero, J.2005The quantitative trait linkage disequilibrium test: a more powerful alternative to the quantitative transmission disequilibrium test for use in the absence of population stratificationS91 BMC Genet 6 Suppl 1Dec 30~ABSTRACT : Linkage analysis based on identity-by-descent allele-sharing can be used to identify a chromosomal region harboring a quantitative trait locus (QTL), but lacks the resolution required for gene identification. Consequently, linkage disequilibrium (association) analysis is often employed for fine-mapping. Variance-components based combined linkage and association analysis for quantitative traits in sib pairs, in which association is modeled as a mean effect and linkage is modeled in the covariance structure has been extended to general pedigrees (quantitative transmission disequilibrium test, QTDT). The QTDT approach accommodates data not only from parents and siblings, but also from all available relatives. QTDT is also robust to population stratification. However, when population stratification is absent, it is possible to utilize even more information, namely the additional information contained in the founder genotypes. In this paper, we introduce a simple modification of the allelic transmission scoring method used in the QTDT that results in a more powerful test of linkage disequilibrium, but is only applicable in the absence of population stratification. This test, the quantitative trait linkage disequilibrium (QTLD) test, has been incorporated into a new procedure in the statistical genetics computer package SOLAR. We apply this procedure in a linkage/association analysis of an electrophysiological measurement previously shown to be related to alcoholism. We also demonstrate by simulation the increase in power obtained with the QTLD test, relative to the QTDT, when a true association exists between a marker and a QTL.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16451707 &1471-2156 (Electronic) Journal article16451707sDepartment of Genetics, Southwest Foundation for Biomedical Research San Antonio, TX, USA. lhavill@darwin.sfbr.org.l~?5Hawley, M. E. Kidd, K. K.1995\HAPLO: a program using the EM algorithm to estimate the frequencies of multi-site haplotypes409-11J Hered865u*Algorithms Alleles Animals DNA/*genetics *Gene Frequency Haplotypes/*genetics Humans Phenotype Probability *SoftwareSep-Octehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7560877 o0022-1503 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.7560877ZDepartment of Genetics, Yale University School of Medicine, New Haven, CT 06510-8005, USA.f?6 Hays, W.L. 1988Statistics, 4th Edn. New York, NY.Holt, Rinehart and Winston5~?7 Hazel, L. N.19434The Genetic Basis for Constructing Selection Indexes476-90Genetics286Novfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17247099 !0016-6731 (Print) Journal Article17247099Iowa State College, Ames, Iowa.~?8Heath, A. C. Eaves, L. J.1985JResolving the effects of phenotype and social background on mate selection15-30 Behav Genet151>*Environment Female *Marriage *Models, Genetic Pregnancy TwinsJanehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=4039132 g0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.4039132~?94Heath, A. C. Kendler, K. S. Eaves, L. J. Markell, D.1985aThe resolution of cultural and biological inheritance: informativeness of different relationships439-65 Behav Genet155^Adoption Computers Culture *Genetics, Medical Humans Models, Theoretical Research Design TwinsSepehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=4074271 g0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.4074271L~?:THeath, A. C. Todorov, A. A. Nelson, E. C. Madden, P. A. Bucholz, K. K. Martin, N. G.2002Gene-environment interaction effects on behavioral variation and risk of complex disorders: the example of alcoholism and other psychiatric disorders30-7Twin Res51Alcoholism/epidemiology/*genetics Analysis of Variance *Environment Female Genetic Predisposition to Disease Genotype Humans Male Mental Disorders/epidemiology/*genetics Quantitative Trait, Heritable Questionnaires Risk FactorsFebThere have been few replicated examples of genotype x environment interaction effects on behavioral variation or risk of psychiatric disorder. We review some of the factors that have made detection of genotype x environment interaction effects difficult, and show how genotype x shared environment interaction (GxSE) effects are commonly confounded with genetic parameters in data from twin pairs reared together. Historic data on twin pairs reared apart can in principle be used to estimate such GxSE effects, but have rarely been used for this purpose. We illustrate this using previously published data from the Swedish Adoption Twin Study of Aging (SATSA), which suggest that GxSE effects could account for as much as 25% of the total variance in risk of becoming a regular smoker. Since few separated twin pairs will be available for study in the future, we also consider methods for modifying variance components linkage analysis to allow for environmental interactions with linked loci.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11893279 Q1369-0523 (Print) Journal Article Research Support, U.S. Gov't, P.H.S. Twin Study11893279Missouri Alcoholism Research Center, Department of Psychiatry, Washington University School of Medicine, St Louis 63108, USA. andrew@matlock.wustl.edu~?; Heath, S. C.1997OMarkov chain Monte Carlo segregation and linkage analysis for oligogenic models748-60Am J Hum Genet613uComputer Simulation Humans *Linkage (Genetics) *Markov Chains *Models, Genetic *Monte Carlo Method Pedigree PhenotypeSepA new method for segregation and linkage analysis, with pedigree data, is described. Reversible jump Markov chain Monte Carlo methods are used to implement a sampling scheme in which the Markov chain can jump between parameter subspaces corresponding to models with different numbers of quantitative-trait loci (QTL's). Joint estimation of QTL number, position, and effects is possible, avoiding the problems that can arise from misspecification of the number of QTL's in a linkage analysis. The method is illustrated by use of a data set simulated for the 9th Genetic Analysis Workshop; this data set had several oligogenic traits, generated by use of a 1,497-member pedigree. The mixing characteristics of the method appear to be good, and the method correctly recovers the simulated model from the test data set. The approach appears to have great potential both for robust linkage analysis and for the answering of more general questions regarding the genetic control of complex traits.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9326339 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9326339_Department of Statistics, University of Washington, Seattle, USA. heath@linkage.rockefeller.eduq~?<Hedrick, P. W.19875Gametic disequilibrium measures: proceed with caution331-41Genetics1172b*Alleles Animals Heterozygote Linkage (Genetics) Mathematics *Models, Genetic Variation (Genetics)OctjFive different measures of gametic disequilibrium in current use and a new one based on R. C. Lewontin's D', are examined and compared. All of them, except the measure based on Lewontin's D', are highly dependent upon allelic frequencies, including four measures that are normalized in some manner. In addition, the measures suggested by A. H. D. Brown, M. F. Feldman and E. Nevo, and T. Ohta can have negative values when there is maximum disequilibrium and have rates of decay in infinite populations that are a function of the initial gametic array. The variances were large for all the measures in samples taken from populations at equilibrium under neutrality, with the measure based on D' having the lowest variance. In these samples, three of the measures were highly correlated, D2, D (equal to the correlation coefficient when there are two alleles at each locus) and the measure X(2) of Brown et al. Using frequency-dependent measures may result in mistaken conclusions, a fact illustrated by discussion of studies inferring recombinational hot spots and the effects of population bottlenecks from disequilibrium values.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3666445 g0016-6731 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.3666445ADepartment of Genetics, University of California, Berkeley 94720.~?=Hedrick, P. W. Thomson, G.1986OA two-locus neutrality test: applications to humans, E. coli and lodgepole pine135-56Genetics1121r*Alleles Escherichia coli/*genetics Humans Mathematics *Models, Genetic Plants/*genetics Species Specificity TreesJanThe expected disequilibrium between two loci with k alleles at one locus and l alleles at the other is given for a sample of size n drawn from a population under neutrality equilibrium. Three different measures of disequilibrium with 95% intervals are tabulated for combinations of n, k, l and 4Nc, where N is the effective population size and c is the amount of recombination between the loci. The extent and pattern of disequilibrium are strongly dependent upon 4Nc and are somewhat dependent on n, k and l. The 95% intervals are large, particularly for low numbers of alleles and low values of 4Nc. As examples, observed disequilibrium from histocompatibility loci in humans (HLA) and electrophoretic data in E. coli and lodgepole pine were compared to these theoretical values. Using information about recombination rates, the HLA data showed more disequilibrium than neutrality expectations, whereas electrophoretic data from E. coli and lodgepole pine had somewhat less disequilibrium than neutrality expectations.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3510942 F0016-6731 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.3510942R~?>Herbert, A. Gerry, N. P. McQueen, M. B. Heid, I. M. Pfeufer, A. Illig, T. Wichmann, H. E. Meitinger, T. Hunter, D. Hu, F. B. Colditz, G. Hinney, A. Hebebrand, J. Koberwitz, K. Zhu, X. Cooper, R. Ardlie, K. Lyon, H. Hirschhorn, J. N. Laird, N. M. Lenburg, M. E. Lange, C. Christman, M. F.2006GA common genetic variant is associated with adult and childhood obesity279-83Science3125771Adult African Americans Alleles *Body Mass Index Case-Control Studies Child Cohort Studies Europe European Continental Ancestry Group Female Gene Frequency Genes, Recessive Genetic Predisposition to Disease Genotype Haplotypes Humans Intracellular Signaling Peptides and Proteins/genetics Linkage Disequilibrium Male Membrane Proteins/genetics Models, Genetic Obesity/*genetics Oligonucleotide Array Sequence Analysis *Polymorphism, Single Nucleotide *Variation (Genetics)Apr 14aObesity is a heritable trait and a risk factor for many common diseases such as type 2 diabetes, heart disease, and hypertension. We used a dense whole-genome scan of DNA samples from the Framingham Heart Study participants to identify a common genetic variant near the INSIG2 gene associated with obesity. We have replicated the finding in four separate samples composed of individuals of Western European ancestry, African Americans, and children. The obesity-predisposing genotype is present in 10% of individuals. Our study suggests that common genetic polymorphisms are important determinants of obesity.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16614226 l1095-9203 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16614226Department of Genetics and Genomics, Boston University Medical School, E613, 715 Albany Street, Boston, MA 02118, USA. aherbert@bu.edu5~??fHeutink, P. van de Wetering, B. J. Pakstis, A. J. Kurlan, R. Sandor, P. Oostra, B. A. Sandkuijl, L. A.1995RLinkage studies on Gilles de la Tourette syndrome: what is the strategy of choice?465-73Am J Hum Genet572uChromosome Mapping Gene Frequency Humans *Linkage (Genetics) Lod Score Pedigree Phenotype Tourette Syndrome/*geneticsAugFor a linkage study it is important to ascertain family material that is sufficiently informative. The statistical power of a linkage sample can be determined via computer simulation. For complex traits uncertain parameters such as incomplete penetrance, frequency of phenocopies, gene frequency and variable expression have to be taken into account. One can either include only the most severe phenotype in the analysis or apply multiple linkage tests for a gradually broadened disease phenotype. Gilles de la Tourette syndrome (GTS) is a chronic neurological disorder characterized by multiple, intermittent motor and vocal tics. Segregation analyses suggest that GTS and milder phenotypes are caused by a single dominant gene. We report here the results of an extensive simulation study on a large set of families. We compared the effectiveness of linkage tests with only the GTS phenotype versus multiple tests that included various milder phenotypes and different gene frequencies. The scenario of multiple tests yielded superior power. Our results show that computer simulation can indicate the strategy of choice in linkage studies of multiple, complex phenotypes.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7668273 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.7668273ODepartment of Clinical Genetics, Erasmus University Rotterdam, The Netherlands.~?@Hewitt, J. K. Heath, A. C.1988MA note on computing the chi-square noncentrality parameter for power analyses105-8 Behav Genet181=Humans *Models, Genetic Research Design/standards *StatisticsJanehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3365192 F0001-8244 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.3365192~?AHHiguchi, S. Matsushita, S. Imazeki, H. Kinoshita, T. Takagi, S. Kono, H.19947Aldehyde dehydrogenase genotypes in Japanese alcoholics741-2Lancet3438899mAlcoholism/ethnology/*genetics Aldehyde Dehydrogenase/*genetics Female Genotype Humans Japan Male Middle AgedMar 19ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7907720 0140-6736 (Print) Letter7907720F~?B Hill, A. B.19656The Environment and Disease: Association or Causation?295-300Proc R Soc Med58,*Environmental Health *Occupational MedicineMayfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14283879 !0035-9157 (Print) Journal Article14283879~?C Hill, W. G.1977RCorrelation of gene frequencies between neutral linked genes in finite populations239-48Theor Popul Biol112rAlleles Computers *Gene Frequency Genes *Linkage (Genetics) *Models, Biological Probability Recombination, GeneticAprdhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=867287 !0040-5809 (Print) Journal Article867287{~?DHill, W. G. Robertson, A.1968=The effects of inbreeding at loci with heterozygote advantage615-28Genetics603QGene Frequency *Heterozygote *Inbreeding Models, Biological *Selection (Genetics)Novehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=5728744 !0016-6731 (Print) Journal Article5728744?EHinds, D.A. Risch, N. 1996VThe ASPEX package: affected sib-pair exclusion mapping. http://aspex.sourceforge.net/.~?FhHinds, D. A. Stuve, L. L. Nilsen, G. B. Halperin, E. Eskin, E. Ballinger, D. G. Frazer, K. A. Cox, D. R.2005HWhole-genome patterns of common DNA variation in three human populations1072-9Science3075712African Americans/*genetics Algorithms Asian Continental Ancestry Group/*genetics Case-Control Studies Chromosome Mapping Databases, Genetic European Continental Ancestry Group/*genetics Female Gene Frequency Genetic Markers Genetic Predisposition to Disease *Genome, Human Genotype Haplotypes Humans Linkage Disequilibrium Male Multifactorial Inheritance *Polymorphism, Single Nucleotide Recombination, Genetic Risk Factors Selection (Genetics) *Variation (Genetics)Feb 18Individual differences in DNA sequence are the genetic basis of human variability. We have characterized whole-genome patterns of common human DNA variation by genotyping 1,586,383 single-nucleotide polymorphisms (SNPs) in 71 Americans of European, African, and Asian ancestry. Our results indicate that these SNPs capture most common genetic variation as a result of linkage disequilibrium, the correlation among common SNP alleles. We observe a strong correlation between extended regions of linkage disequilibrium and functional genomic elements. Our data provide a tool for exploring many questions that remain regarding the causal role of common human DNA variation in complex human traits and for investigating the nature of genetic variation within and between human populations.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15718463 G1095-9203 (Electronic) Journal Article Research Support, Non-U.S. Gov't15718463JPerlegen Sciences Inc., 2021 Stierlin Court, Mountain View, CA 94043, USA. ~?GmHinrichs, A. S. Karolchik, D. Baertsch, R. Barber, G. P. Bejerano, G. Clawson, H. Diekhans, M. Furey, T. S. Harte, R. A. Hsu, F. Hillman-Jackson, J. Kuhn, R. M. Pedersen, J. S. Pohl, A. Raney, B. J. Rosenbloom, K. R. Siepel, A. Smith, K. E. Sugnet, C. W. Sultan-Qurraie, A. Thomas, D. J. Trumbower, H. Weber, R. J. Weirauch, M. Zweig, A. S. Haussler, D. Kent, W. J.2006-The UCSC Genome Browser Database: update 2006D590-8Nucleic Acids Res34Database issueAmino Acid Sequence Animals California Computer Graphics *Databases, Genetic Dogs Gene Expression Genes *Genomics Humans Internet Mice Polymorphism, Single Nucleotide Proteins/chemistry/genetics/metabolism Proteomics Rats Sequence Alignment Software User-Computer InterfaceJan 1/The University of California Santa Cruz Genome Browser Database (GBD) contains sequence and annotation data for the genomes of about a dozen vertebrate species and several major model organisms. Genome annotations typically include assembly data, sequence composition, genes and gene predictions, mRNA and expressed sequence tag evidence, comparative genomics, regulation, expression and variation data. The database is optimized to support fast interactive performance with web tools that provide powerful visualization and querying capabilities for mining the data. The Genome Browser displays a wide variety of annotations at all scales from single nucleotide level up to a full chromosome. The Table Browser provides direct access to the database tables and sequence data, enabling complex queries on genome-wide datasets. The Proteome Browser graphically displays protein properties. The Gene Sorter allows filtering and comparison of genes by several metrics including expression data and several gene properties. BLAT and In Silico PCR search for sequences in entire genomes in seconds. These tools are highly integrated and provide many hyperlinks to other databases and websites. The GBD, browsing tools, downloadable data files and links to documentation and other information can be found at http://genome.ucsc.edu/.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16381938 l1362-4962 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16381938Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), Santa Cruz, CA 95064, USA. angie@soe.ucsc.eduy~?HHirschhorn, J. N. Daly, M. J.2005FGenome-wide association studies for common diseases and complex traits95-108 Nat Rev Genet62Animals *Genetic Predisposition to Disease *Genome Humans Linkage Disequilibrium/*genetics Multifactorial Inheritance/genetics *Quantitative Trait, Heritable Sequence Analysis, DNA/methods Variation (Genetics)/*geneticsFebGenetic factors strongly affect susceptibility to common diseases and also influence disease-related quantitative traits. Identifying the relevant genes has been difficult, in part because each causal gene only makes a small contribution to overall heritability. Genetic association studies offer a potentially powerful approach for mapping causal genes with modest effects, but are limited because only a small number of genes can be studied at a time. Genome-wide association studies will soon become possible, and could open new frontiers in our understanding and treatment of disease. However, the execution and analysis of such studies will require great care.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15716906 I1471-0056 (Print) Journal Article Research Support, Non-U.S. Gov't Review15716906Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02139, USA. joel.hirschhorn@childrens.harvard.edu~?I9Hirschhorn, J. N. Lohmueller, K. Byrne, E. Hirschhorn, K.20025A comprehensive review of genetic association studies45-61 Genet Med42Alleles Disease Susceptibility Environment Genetic Diseases, Inborn/*genetics *Genetic Predisposition to Disease Genetics, Population Genome, Human Guidelines Humans Linkage Disequilibrium *Polymorphism, Single Nucleotide Variation (Genetics)Mar-Apr=Most common diseases are complex genetic traits, with multiple genetic and environmental components contributing to susceptibility. It has been proposed that common genetic variants, including single nucleotide polymorphisms (SNPs), influence susceptibility to common disease. This proposal has begun to be tested in numerous studies of association between genetic variation at these common DNA polymorphisms and variation in disease susceptibility. We have performed an extensive review of such association studies. We find that over 600 positive associations between common gene variants and disease have been reported; these associations, if correct, would have tremendous importance for the prevention, prediction, and treatment of most common diseases. However, most reported associations are not robust: of the 166 putative associations which have been studied three or more times, only 6 have been consistently replicated. Interestingly, of the remaining 160 associations, well over half were observed again one or more times. We discuss the possible reasons for this irreproducibility and suggest guidelines for performing and interpreting genetic association studies. In particular, we emphasize the need for caution in drawing conclusions from a single report of an association between a genetic variant and disease susceptibility.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11882781 I1098-3600 (Print) Journal Article Research Support, Non-U.S. Gov't Review11882781MWhitehead Institute/MIT Center for Genome Research, Cambridge, MA 02139, USA. ~?J*Hodge, S. E. Abreu, P. C. Greenberg, D. A.1997iMagnitude of type I error when single-locus linkage analysis is maximized over models: a simulation study217-27Am J Hum Genet601X*Computer Simulation *Linkage (Genetics) Lod Score *Models, Genetic *Models, StatisticalJanIt is well known that maximizing the maximum LOD score over multiple parameter values or models (i.e., the method of mod scores, or MMLS), will inflate type I error, compared with assuming only one parameter value/model in the linkage analysis. On the other hand, a mod score often has greater power to detect linkage than does a LOD score (Z) calculated under a wrong genetic model. Therefore, it is of interest to determine the actual magnitude of type I error in realistic genetic situations. Simulated data sets with no linkage were generated under three dominant and three recessive single-locus models, with reduced penetrance (f = .8, .5, and .2). Data sets were analyzed for linkage by (1) maximizing over penetrance only, (2) maximizing over "dominance model" (i.e., dominant versus recessive), and (3) maximizing over both penetrance and dominance model simultaneously. In (1), the resultant significance levels were approximately doubled, compared with baseline values if one had not maximized over penetrances (i.e., compared with a one-sided chi2(1)). In (2), significance levels were increased somewhat less, and, in (3), they were increased by approximately two to three times (but not more than four times) over those of the one-sided chi2(1). This means that, for a given size of test alpha, an investigator would need to increase the Z used as a test criterion, by approximately 0.30 LOD units for analyses as in (1) or (2) and by 0.60 Z units for analyses as in (3). These guidelines, which are valid up to approximately Z = 3.0, are conservative for (1) and are very conservative for (2) and (3). By quantifying the increase in significance level (or, correspondingly, the increase in Z), our findings will enable users to rationally assess the advantages versus the disadvantages of mod scores.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8981965 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.8981965eDepartment of Psychiatry, Columbia University, New York, NY 10032, USA. hodge@child.cpmc.columbia.edu~?K Holmans, P.1993;Asymptotic properties of affected-sib-pair linkage analysis362-74Am J Hum Genet522Chi-Square Distribution Family Health Gene Frequency Humans Likelihood Functions *Linkage (Genetics) Lod Score Mathematics *Models, Genetic Multiple Sclerosis/genetics Parents Polymorphism, Genetic Receptors, Antigen, T-Cell/geneticsFeb The likelihood-ratio method for affected-sib-pair analysis, introduced by Risch, is a powerful method for detecting linkage when the marker is not perfectly polymorphic, as is often the case. The power of this method can be improved by restricting maximization to the set of possible haplotype-sharing probabilities--denoted the "possible triangle" method. The asymptotic distributions of the resulting distributions are derived, enabling test criteria to be found for any required test size (i.e., the probability of falsely detecting linkage when none exists) and enabling p values to be assigned to results. The criteria were found to be approximately constant when the PIC of the marker varies, making them applicable to any marker. The asymptotic power approximations were used to investigate the relative performance of pairs with typed parents, relative to those without, by comparing the sample sizes necessary for a given power. Under certain circumstances, typing the parents proved to be inefficient, even when PIC was low.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8430697 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't8430697XMedical Research Council Biostatistics Unit, University Forvie Site, Cambridge, England.~?LHolmans, P. Clayton, D.1995Efficiency of typing unaffected relatives in an affected-sib-pair linkage study with single-locus and multiple tightly linked markers1221-32Am J Hum Genet575Alleles Genetic Diseases, Inborn/*genetics Genetic Markers Haplotypes Humans Likelihood Functions *Linkage (Genetics) Lod Score Sample SizeNovIn an affected-sib-pair study, the parents are often unavailable for typing, particularly for diseases of late onset. In many cases, however, it is possible to sample unaffected siblings. It is therefore desirable to assess the contribution of such siblings to the power of such a study. The likelihood ratio introduced by Risch and improved by Holmans was extended to incorporate data from unaffected siblings. Tests based on two likelihoods were considered: the full likelihood of the data, based on the identity-by-descent (IBD) sharing states of the entire sibship, and a pseudolikelihood based on the IBD sharing states of the affected pair only, using the unaffected siblings to infer parental genotypes. The latter approach was found to be more powerful, except when penetrance was high. Typing an unaffected sibling, or just one parent, was found to give only a small increase in power except when the PIC of the marker was low. Even then, typing an unaffected relative increased the overall number of individuals that had to be typed to achieve a given power. If there is no highly informative marker locus in the area under study, it may be possible to "build" one by combining the alleles from two or more neighboring tightly linked loci into haplotypes. Typing two loci gave a sizeable power increase over a single locus, but typing further loci gave much smaller gains. Building haplotypes will introduce phase uncertainties, with the result that such a system will yield less power than will a single locus with the same number of alleles. This power loss was small, however, and did not affect the conclusions regarding the worth of typing unaffected relatives.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7485174 !0002-9297 (Print) Journal Article7485174WDepartment of Psychological Medicine, University of Wales College of Medicine, Cardiff. ~?MUHoppe, B. Haupl, T. Gruber, R. Kiesewetter, H. Burmester, G. R. Salama, A. Dorner, T.2006Detailed analysis of the variability of peptidylarginine deiminase type 4 in German patients with rheumatoid arthritis: a case-control studyR34Arthritis Res Ther82Adolescent Adult Aged Aged, 80 and over Alleles Antibodies/blood Arthritis, Rheumatoid/blood/*enzymology/*genetics/physiopathology Case-Control Studies Child Female Gene Frequency Genetic Predisposition to Disease Genotype Germany HLA-DR Antigens/genetics Haplotypes Heterozygote Humans Hydrolases/*genetics Male Middle Aged Peptides, Cyclic/immunology Polymorphism, Single Nucleotide *Variation (Genetics):Peptidylarginine deiminase type 4 (PADI4) genotypes were shown to influence susceptibility to rheumatoid arthritis (RA) in the Japanese population. Such an association could not previously be confirmed in different European populations. In the present study, we analysed exons 2-4 of PADI4 in 102 German RA patients and 102 healthy individuals to study the influence of PADI4 variability on RA susceptibility by means of haplotype-specific DNA sequencing. Analyses of the influence of PADI4 and HLA-DRB1 genotypes on disease activity and on levels of anti-cyclic citrullinated peptide antibodies were performed.Comparing the frequencies of PADI4 haplotype 4 (padi4_89*G, padi4_90*T, padi4_92*G, padi4_94*T, padi4_104*C, padi4_95*G, padi4_96*T) (patients, 14.7%; controls, 7.8%; odds ratio = 2.0, 95% confidence interval = 1.1-3.8) and carriers of this haplotype (patients, 27.5%; controls, 13.7%; odds ratio = 2.4, 95% confidence interval = 1.2-4.8), a significant positive association of PADI4 haplotype 4 with RA could be demonstrated. Other PADI4 haplotypes did not differ significantly between patients and controls. Regarding the individual PADI4 variants, padi4_89 (A-->G), padi4_90 (C-->T), and padi4_94 (C-->T) were significantly associated with RA (patients, 49.5%; controls, 38.7%; odds ratio = 1.6, 95% confidence interval = 1.1-2.3). Considering novel PADI4 variants located in or near to exons 2, 3, and 4, no quantitative or qualitative differences between RA patients (8.8%) and healthy controls (10.8%) could be demonstrated. While the PADI4 genotype did not influence disease activity and the anti-cyclic citrullinated peptide antibody level, the presence of the HLA-DRB1 shared epitope was significantly associated with higher anti-cyclic citrullinated peptide antibody levels (P = 0.033).The results of this small case-control study support the hypothesis that variability of the PADI4 gene may influence susceptibility to RA in the German population. Quantitative or qualitative differences in previously undefined PADI4 variants between patients and controls could not be demonstrated.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16469113 &1478-6362 (Electronic) Journal Article16469113Institute of Transfusion Medicine, Campus Virchow-Klinikum, Charite-Universitatsmedizin Berlin, Germany. berthold.hoppe@charite.deC~?NHopper, J. L. Mathews, J. D.1982>Extensions to multivariate normal models for pedigree analysis373-83 Ann Hum Genet46Pt 4NFamily Genetic Markers Humans *Models, Genetic *Pedigree *Variation (Genetics)OctvLange, Westlake & Spence (1976) used the assumption of multivariate normality to apply a likelihood method to the analysis of quantitative traits measured over pedigrees. We now introduce a test of the assumption of multivariate normality and methods for the detection of outlying families and outlying individuals. We also introduce a method for the estimation of effects of measured genetic markers as variance components, a flexible parameterization to estimate effects of shared family environment, and a method to allow for the ascertainment of pedigrees through probands. These innovations have been applied using numerical methods for maximization of the likelihood. Simulation studies and available theory suggest that the likelihood ratio criterion used in significance testing follows the expected asymptotic distribution with sample sizes encountered in typical applications.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6961886 g0003-4800 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.6961886~?OHorsthemke, B. Buiting, K.2006)Imprinting defects on human chromosome 15292-9Cytogenet Genome Res1131-4Angelman Syndrome/genetics *Chromosome Mapping *Chromosomes, Human, Pair 15 *DNA Methylation Female Genetic Diseases, Inborn/*genetics *Genomic Imprinting Humans Male Prader-Willi Syndrome/genetics Sequence DeletionThe Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are two distinct neurogenetic diseases that are caused by the loss of function of imprinted genes on the proximal long arm of human chromosome 15. In a few percent of patients with PWS and AS, the disease is due to aberrant imprinting and gene silencing. In patients with PWS and an imprinting defect, the paternal chromosome carries a maternal imprint. In patients with AS and an imprinting defect, the maternal chromosome carries a paternal imprint. Imprinting defects offer a unique opportunity to identify some of the factors and mechanisms involved in imprint erasure, resetting and maintenance. In approximately 10% of cases the imprinting defects are caused by a microdeletion affecting the 5' end of the SNURF-SNRPN locus. These deletions define the 15q imprinting center (IC), which regulates imprinting in the whole domain. These findings have been confirmed and extended in knock-out and transgenic mice. In the majority of patients with an imprinting defect, the incorrect imprint has arisen without a DNA sequence change, possibly as the result of stochastic errors of the imprinting process or the effect of exogenous factors.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16575192 -1424-859X (Electronic) Journal Article Review16575192`Institut fur Humangenetik, Universitatsklinikum Essen, Essen, Germany. b.horsthemke@uni-essen.de~?PHorvath, S. Laird, N. M.1998SA discordant-sibship test for disequilibrium and linkage: no need for parental data1886-97Am J Hum Genet636Alleles Female Genetic Markers *Genetic Screening Genotype Humans Linkage (Genetics)/*genetics *Linkage Disequilibrium Male *Nuclear Family Parents Sample Size Statistics, NonparametricDec,The sibship disequilibrium test (SDT) is designed to detect both linkage in the presence of association and association in the presence of linkage (linkage disequilibrium). The test does not require parental data but requires discordant sibships with at least one affected and one unaffected sibling. The SDT has many desirable properties: it uses all the siblings in the sibship; it remains valid if there are misclassifications of the affectation status; it does not detect spurious associations due to population stratification; asymptotically it has a chi2 distribution under the null hypothesis; and exact P values can be easily computed for a biallelic marker. We show how to extend the SDT to markers with multiple alleles and how to combine families with parents and data from discordant sibships. We discuss the power of the test by presenting sample-size calculations involving a complex disease model, and we present formulas for the asymptotic relative efficiency (which is approximately the ratio of sample sizes) between SDT and the transmission/disequilibrium test (TDT) for special family structures. For sib pairs, we compare the SDT to a test proposed both by Curtis and, independently, by Spielman and Ewens. We show that, for discordant sib pairs, the SDT has good power for testing linkage disequilibrium relative both to Curtis's tests and to the TDT using trios comprising an affected sib and its parents. With additional sibs, we show that the SDT can be more powerful than the TDT for testing linkage disequilibrium, especially for disease prevalence >.3.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9837840 X0002-9297 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.9837840JDepartment of Biostatistics, Harvard School of Public Health, Boston, USA.<~?QIHorvath, S. Xu, X. Lake, S. L. Silverman, E. K. Weiss, S. T. Laird, N. M.2004iFamily-based tests for associating haplotypes with general phenotype data: application to asthma genetics61-9Genet Epidemiol261Algorithms Asthma/*genetics Family Family Health Gene Frequency *Haplotypes Humans Phenotype Polymorphism, Single Nucleotide Receptors, Adrenergic, beta/geneticsJanNWe provide a general purpose family-based testing strategy for associating disease phenotypes with haplotypes when phase may be ambiguous and parental genotype data may be missing. These tests for linkage and association can be used in candidate gene studies with tightly linked markers. Our proposed weighted conditional approach extends the method described in Rabinowitz and Laird to multiple markers. It is attractive because it provides haplotype tests for family-based studies that are efficient and robust to population admixture, phenotype distribution specification, and ascertainment based on phenotypes. It can handle missing parental genotypes and/or missing phase in both offspring and parents. It yields either haplotype-specific (univariate) tests or multi-haplotype (global) tests. This extension has been implemented in the freely available software haplotype FBAT. We used the haplotype FBAT program to test for associations between asthma phenotypes and single nucleotide polymorphisms (SNPs) in the beta-2 adrenergic receptor gene. Whereas no single SNP showed significant association with asthma diagnosis or bronchodilator responsiveness (quantitative trait), a haplotype-based global test found a highly significant association with asthma diagnosis (P value <0.00005) and the measure of bronchodilator responsiveness (P value =0.016).fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14691957 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.14691957Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA. shorvath@mednet.ucla.edu :~?R_Hottenga, J. J. Boomsma, D. I. Kupper, N. Posthuma, D. Snieder, H. Willemsen, G. de Geus, E. J.20054Heritability and stability of resting blood pressure499-508Twin Res Hum Genet85Adolescent Adult Aged Analysis of Variance Blood Pressure/*genetics Blood Pressure Determination Environment Female Humans Male Middle Aged Twins, Dizygotic Twins, MonozygoticOct|We examined the contribution of genetic and environmental influences to variation in resting systolic (SBP) and diastolic (DBP) blood pressure in participants from 4 twin studies carried out between 1986 and 2003. A total of 1577 subjects (682 males, 895 females) participated. There were 580 monozygotic twins, 664 dizygotic twins and 333 of their siblings. The 4 studies sampled subjects in different age groups (average age 17, 32, 37, 44 years), allowing for comparison of the relative contribution of genetic and environmental factors across the first part of the life span. Blood pressure was assessed under laboratory conditions in 3 studies and by ambulatory monitoring in 1 study. Univariate analyses of SBP and DBP showed significant heritability of blood pressure in all studies (SBP h(2) 48% to 60%, DBP h(2) 34% to 67%). Overall, there was little evidence for sex differences in blood pressure heritability, and no evidence for differences in heritability due to measurement strategy (laboratory vs. ambulatory). For 431 subjects there were data from 2 or more occasions that allowed us to assess the tracking of blood pressure over time and to estimate the genetic and environmental contributions to blood pressure tracking. Correlations over time across an average period of 7.1 years (tracking) were between .41 and .70. Multivariate genetic analyses showed that blood pressure tracking was entirely explained by the same genetic factors being expressed across time. It was concluded that whole genome scans for resting blood pressure can safely pool data from males and females, laboratory and ambulatory recordings, and different age cohorts.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16212839 M1832-4274 (Print) Journal Article Research Support, Non-U.S. Gov't Twin Study16212839jDepartment of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands. jj.hottenga@psy.vu.nl?SHuber, P1967JThe behaviour of maximum likelihood estimates under nonstandard conditions221-233]Proceedings of the Fifth Berkeley Symposium in Mathematical Statistics and Probability, Vol 1 Berkeley, CA,University of California Press, 7~?THugot, J. P. Chamaillard, M. Zouali, H. Lesage, S. Cezard, J. P. Belaiche, J. Almer, S. Tysk, C. O'Morain, C. A. Gassull, M. Binder, V. Finkel, Y. Cortot, A. Modigliani, R. Laurent-Puig, P. Gower-Rousseau, C. Macry, J. Colombel, J. F. Sahbatou, M. Thomas, G.2001WAssociation of NOD2 leucine-rich repeat variants with susceptibility to Crohn's disease599-603Nature4116837Alleles *Carrier Proteins Chromosomes, Human, Pair 16 Cloning, Molecular Colitis, Ulcerative/genetics Crohn Disease/etiology/*genetics Gene Frequency Genetic Predisposition to Disease Genotype Humans *Intracellular Signaling Peptides and Proteins Leucine Linkage (Genetics) NF-kappa B/metabolism Nod2 Signaling Adaptor Protein Polymorphism, Single Nucleotide Proteins/*genetics Repetitive Sequences, Amino Acid Signal Transduction Variation (Genetics)May 31Crohn's disease and ulcerative colitis, the two main types of chronic inflammatory bowel disease, are multifactorial conditions of unknown aetiology. A susceptibility locus for Crohn's disease has been mapped to chromosome 16. Here we have used a positional-cloning strategy, based on linkage analysis followed by linkage disequilibrium mapping, to identify three independent associations for Crohn's disease: a frameshift variant and two missense variants of NOD2, encoding a member of the Apaf-1/Ced-4 superfamily of apoptosis regulators that is expressed in monocytes. These NOD2 variants alter the structure of either the leucine-rich repeat domain of the protein or the adjacent region. NOD2 activates nuclear factor NF-kB; this activating function is regulated by the carboxy-terminal leucine-rich repeat domain, which has an inhibitory role and also acts as an intracellular receptor for components of microbial pathogens. These observations suggest that the NOD2 gene product confers susceptibility to Crohn's disease by altering the recognition of these components and/or by over-activating NF-kB in monocytes, thus documenting a molecular model for the pathogenic mechanism of Crohn's disease that can now be further investigated.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11385576 B0028-0836 (Print) Journal Article Research Support, Non-U.S. Gov't11385576@Fondation Jean Dausset CEPH, 27 rue J. Dodu 75010 Paris, France.J~?UIdury, R. M. Cardon, L. R.1997FA simple method for automated allele binning in microsatellite markers1104-9 Genome Res711!*Alleles Automation Computer Simulation Dinucleotide Repeats Genetic Diseases, Inborn/genetics Genetic Markers *Genome, Human Genotype Humans Least-Squares Analysis *Microsatellite Repeats Models, Genetic Models, Statistical Polymerase Chain Reaction/methods Software Trinucleotide RepeatsNovHigh-throughput fluorescent genotyping requires a considerable amount of automation for accurate and efficient processing of genetic markers. Automated DNA sequencers and corresponding software products are commercially available that contribute substantially to increased throughput rates for large-scale genotyping projects. However, some conceptually simple tasks still require time-consuming manual intervention that imposes bottlenecks on throughput capacity. One of these tasks is the conversion of imprecise DNA fragment sizes determined by commercial software programs to the underlying discrete alleles that the sizes represent. Here we describe a simple method for assigning allele sizes into their appropriate allele "bins" using least-squares minimization procedures. The method requires no special treatment of family data on plates, internal/external size standards, or electropherogram data manipulation. Tests of the method using the ABI 373A automated DNA sequencer and accompanying Genescan/Genotyper software resulted in accurate automatic classification of all alleles in >80% of 208 markers analyzed, with the remaining 20% being appropriately identified as requiring additional attention to laboratory conditions. Specific characteristics of different markers, including differences in PCR product size and inexact repeat lengths (e.g., 1. 9 bp for a dinucleotide repeat), are accommodated by the method and their properties discussed.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9371746 !1088-9051 (Print) Journal Article9371746<Sequana Therapeutics, Inc., La Jolla, California 92037, USA.]~?VIdury, R. M. Elston, R. C.1997^A faster and more general hidden Markov model algorithm for multipoint likelihood calculations197-202 Hum Hered474RAlgorithms Humans Likelihood Functions *Linkage (Genetics) *Markov Chains PedigreeJul-AugbThere are two basic algorithms for calculating multipoint linkage likelihoods: in one the computational effort increases linearly with the number of pedigree members and exponentially with the number of markers, in the other the effort increases exponentially with the number of persons but linearly with the number of markers. We describe a faster version of the latter algorithm for which there is no penalty in making the recombination fraction meiosis specific. This can lead to faster and potentially more powerful linkage analysis whenever the number of nonfounder meioses in a pedigree is not too large.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9239506 F0001-5652 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9239506,Sequana Therapeutics, La Jolla, Calif., USA.!~?W International HapMap Consortium,2005#A haplotype map of the human genome1299-320Nature4377063Chromosomes, Human, Y/genetics DNA, Mitochondrial/genetics Gene Frequency/genetics *Genome, Human Haplotypes/*genetics Humans Linkage Disequilibrium/genetics Polymorphism, Single Nucleotide/*genetics Recombination, Genetic/genetics Selection (Genetics)Oct 27Inherited genetic variation has a critical but as yet largely uncharacterized role in human disease. Here we report a public database of common variation in the human genome: more than one million single nucleotide polymorphisms (SNPs) for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted. These data document the generality of recombination hotspots, a block-like structure of linkage disequilibrium and low haplotype diversity, leading to substantial correlations of SNPs with many of their neighbours. We show how the HapMap resource can guide the design and analysis of genetic association studies, shed light on structural variation and recombination, and identify loci that may have been subject to natural selection during human evolution.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16255080 1476-4687 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.16255080~?XIoannidis, J. P.2005.Why most published research findings are falsee124PLoS Med28*Bias (Epidemiology) *Data Interpretation, Statistical Likelihood Functions Meta-Analysis Odds Ratio *Publishing Reproducibility of Results *Research Design Sample SizeAugThere is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16060722 &1549-1676 (Electronic) Journal Article16060722wDepartment of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece. jioannid@cc.uoi.gr~?Y?Ioannidis, J. P. Rosenberg, P. S. Goedert, J. J. O'Brien, T. R.2002RCommentary: meta-analysis of individual participants' data in genetic epidemiology204-10Am J Epidemiol1563#Bias (Epidemiology) Confounding Factors (Epidemiology) Disease Progression *Epidemiologic Measurements Genetics, Medical/*statistics & numerical data HIV Infections/*epidemiology/*genetics HIV Seropositivity/epidemiology HIV Seroprevalence Humans *Meta-Analysis Receptors, Chemokine/geneticsAug 1}The authors summarize their experience in the conduct of meta-analysis of individual participants' data (MIPD) with time-to-event analyses in the field of genetic epidemiology. The MIPD offers many advantages compared with a meta-analysis of the published literature. These include standardization of case definitions, outcomes, and covariates; inclusion of updated information; the ability to fully test the assumptions of time-to-event models; better control of confounding; standardization of analyses of genetic loci that are in linkage disequilibrium; evaluation of alternative genetic models and multiple genes; consistent treatment of subpopulations; assessment of sampling bias; and the establishment of an international collaboration with the capability to prospectively update the meta-analyses and synthesize new information on multiple genetic loci and outcomes. The disadvantages of a MIPD compared with a meta-analysis of the published literature are that a much greater commitment of time and resources is required to collect primary data and to coordinate a large collaborative project. An MIPD may collect additional, unpublished data, but it is possible that not all published data may be contributed at the individual level. For questions that justify the required intensive effort, the MIPD method is a useful tool to help clarify the role of candidate genes in complex human diseases.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12142254 !0002-9262 (Print) Journal Article12142254Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, and Ioannina Biomedical Research Institute, Foundation for Research and Technology-Hellas, Ioannina, Greece. .~?ZMIoannidis, J. P. Trikalinos, T. A. Ntzani, E. E. Contopoulos-Ioannidis, D. G.2003KGenetic associations in large versus small studies: an empirical assessment567-71Lancet3619357[Alleles Clinical Trials/*methods Genetic Markers Humans *Polymorphism, Genetic *Sample SizeFeb 15BACKGROUND: Advances in human genetics could help us to assess prognosis on an individual basis and to optimise the management of complex diseases. However, different studies on the same genetic association sometimes have discrepant results. Our aim was to assess how often large studies arrive at different conclusions than smaller studies, and whether this situation arises more frequently when findings of first published studies disagree with those of subsequent research. METHODS: We examined the results of 55 meta-analyses (579 study comparisons) of genetic associations and tested whether the magnitude of the genetic effect differs in large versus smaller studies. FINDINGS: We noted significant between-study heterogeneity in 26 (47%) meta-analyses. The magnitude of the genetic effect differed significantly in large versus smaller studies in ten (18%), 20 (36%), and 21 (38%) meta-analyses with tests of rank correlation, regression on SE, and regression on inverse of variance, respectively. The largest studies generally yielded more conservative results than the complete meta-analyses, which included all studies (p=0.005). In 14 (26%) meta-analyses the proposed association was significantly stronger in the first studies than in subsequent research. Only in nine (16%) meta-analyses was the genetic association significant and replicated without hints of heterogeneity or bias. There was little concordance in first versus subsequent discrepancies, and large versus small discrepancies. INTERPRETATION: Genuine heterogeneity and bias could affect the results of genetic association studies. Genetic risk factors for complex diseases should be assessed cautiously and, if possible, using large scale evidence.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12598142 W0140-6736 (Print) Journal Article Meta-Analysis Research Support, Non-U.S. Gov't Review12598142fDepartment of Hygiene, University of Ioannina School of Medicine, Ioannina, Greece. jioannid@cc.uoi.gr{~?[ James, J. W.1971/Frequency in relatives for an all-or-none trait47-9 Ann Hum Genet351iFemale *Genetics, Medical Humans Models, Biological Parents Pregnancy Sibling Relations *Statistics TwinsJulehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=5106369 !0003-4800 (Print) Journal Article5106369~?\Jardine, R. Martin, N. G.1984YNo evidence for sex-linked or sex-limited gene expression influencing spatial orientation345-54 Behav Genet144Environment Female Gene Frequency Humans *Linkage (Genetics) Male Models, Genetic Pregnancy Sex Factors *Space Perception *Spatial Behavior *TwinsJulehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6542357 !0001-8244 (Print) Journal Article6542357~?]&Jeffreys, A. J. Kauppi, L. Neumann, R.2001gIntensely punctate meiotic recombination in the class II region of the major histocompatibility complex217-22 Nat Genet292Crossing Over, Genetic DNA/genetics *Genes, MHC Class II Humans Linkage Disequilibrium Male Meiosis/*genetics Polymorphism, Genetic *Recombination, Genetic Spermatozoa/metabolismOcttThere is considerable interest in understanding patterns of linkage disequilibrium (LD) in the human genome, to aid investigations of human evolution and facilitate association studies in complex disease. The relative influences of meiotic crossover distribution and population history on LD remain unclear, however. In particular, it is uncertain to what extent crossovers are clustered into 'hot spots, that might influence LD patterns. As a first step to investigating the relationship between LD and recombination, we have analyzed a 216-kb segment of the class II region of the major histocompatibility complex (MHC) already characterized for familial crossovers. High-resolution LD analysis shows the existence of extended domains of strong association interrupted by patchwork areas of LD breakdown. Sperm typing shows that these areas correspond precisely to meiotic crossover hot spots. All six hot spots defined share a remarkably similar symmetrical morphology but vary considerably in intensity, and are not obviously associated with any primary DNA sequence determinants of hot-spot activity. These hot spots occur in clusters and together account for almost all crossovers in this region of the MHC. These data show that, within the MHC at least, crossovers are far from randomly distributed at the molecular level and that recombination hot spots can profoundly affect LD patterns.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11586303 B1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't11586303UDepartment of Genetics, University of Leicester, Leicester, LE1 7RH, UK. ajj@le.ac.ukp?^ Jensen, F.V1996%An Introduction to Bayesian Networks.LondonUniversity College Press, u~?_Jinks, J. L. Fulker, D. W.1970iComparison of the biometrical genetical, MAVA, and classical approaches to the analysis of human behavior311-49 Psychol Bull735Adult Analysis of Variance Child Educational Status Environment Extraversion (Psychology) Female *Genetics, Behavioral Genotype Humans Intelligence Intelligence Tests Male Mathematics Models, Psychological Neurotic Disorders Personality Pregnancy *Psychometrics Sex Twins VocabularyMayehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=5528333 !0033-2909 (Print) Journal Article5528333 ~?`Johnson, G. C. Esposito, L. Barratt, B. J. Smith, A. N. Heward, J. Di Genova, G. Ueda, H. Cordell, H. J. Eaves, I. A. Dudbridge, F. Twells, R. C. Payne, F. Hughes, W. Nutland, S. Stevens, H. Carr, P. Tuomilehto-Wolf, E. Tuomilehto, J. Gough, S. C. Clayton, D. G. Todd, J. A.2001@Haplotype tagging for the identification of common disease genes233-7 Nat Genet292Base Sequence Dna *Genetic Predisposition to Disease *Haplotypes Humans Linkage Disequilibrium Polymorphism, Single Nucleotide Sequence Homology, Nucleic AcidOctGenome-wide linkage disequilibrium (LD) mapping of common disease genes could be more powerful than linkage analysis if the appropriate density of polymorphic markers were known and if the genotyping effort and cost of producing such an LD map could be reduced. Although different metrics that measure the extent of LD have been evaluated, even the most recent studies have not placed significant emphasis on the most informative and cost-effective method of LD mapping-that based on haplotypes. We have scanned 135 kb of DNA from nine genes, genotyped 122 single-nucleotide polymorphisms (SNPs; approximately 184,000 genotypes) and determined the common haplotypes in a minimum of 384 European individuals for each gene. Here we show how knowledge of the common haplotypes and the SNPs that tag them can be used to (i) explain the often complex patterns of LD between adjacent markers, (ii) reduce genotyping significantly (in this case from 122 to 34 SNPs), (iii) scan the common variation of a gene sensitively and comprehensively and (iv) provide key fine-mapping data within regions of strong LD. Our results also indicate that, at least for the genes studied here, the current version of dbSNP would have been of limited utility for LD mapping because many common haplotypes could not be defined. A directed re-sequencing effort of the approximately 10% of the genome in or near genes in the major ethnic groups would aid the systematic evaluation of the common variant model of common disease.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11586306 B1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't11586306JDRF/WT Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/Medical Research Council Building, Hills Road, Cambridge, UK.~?a Jorde, L. B.1995-Linkage disequilibrium as a gene-mapping tool11-4Am J Hum Genet561Chromosome Mapping/*methods Ethnic Groups/genetics Gene Frequency Genetic Markers Genome, Human Humans Likelihood Functions *Linkage Disequilibrium Mutation Recombination, GeneticJanehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7825565 *0002-9297 (Print) Comment Editorial Review7825565=~?bJoreskog, K. G.19668Testing a simple structure hypothesis in factor analysis165-78 Psychometrika312*Factor Analysis, StatisticalJunehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=5222205 !0033-3123 (Print) Journal Article5222205|7cJoreskog, K. G.19678Some Contributions to Maximum Likelihood Factor Analysis443-& Psychometrika324://A1967A535100007/A5351 Times Cited:244 Cited References Count:11 0033-3123ISI:A1967A535100007English1|7dJoreskog, K. G. Lawley, D. N.19681New Methods in Maximum Likelihood Factor Analysis85-&8British Journal of Mathematical & Statistical Psychology21://A1968B6709000055Part 1 B6709 Times Cited:75 Cited References Count:14 0007-1102ISI:A1968B670900005English?eJoreskog, K.G. Sorbom, D1993"LISREL 8 User’s Reference Guide. Chicago, IL!Scientific Software International y~?f#Kang, S. J. Gordon, D. Finch, S. J.2004KWhat SNP genotyping errors are most costly for genetic association studies?132-41Genet Epidemiol262 Alleles Case-Control Studies Chromosome Mapping/*statistics & numerical data Gene Frequency/*genetics *Genotype Humans Linkage Disequilibrium/genetics Mathematical Computing Phenotype Polymorphism, Single Nucleotide/*genetics Reproducibility of Results Sample Size Selection Bias SoftwareFeb~Which genotype misclassification errors are most costly, in terms of increased sample size necessary (SSN) to maintain constant asymptotic power and significance level, when performing case/control studies of genetic association? We answer this question for single-nucleotide polymorphisms (SNPs), using the 2x3 chi(2) test of independence. Our strategy is to expand the noncentrality parameter of the asymptotic distribution of the chi(2) test under a specified alternative hypothesis to approximate SSN, using a linear Taylor series in the error parameters. We consider two scenarios: the first assumes Hardy-Weinberg equilibrium (HWE) for the true genotypes in both cases and controls, and the second assumes HWE only in controls. The Taylor series approximation has a relative error of less than 1% when each error rate is less than 2%. The most costly error is recording the more common homozygote as the less common homozygote, with indefinitely increasing cost coefficient as minor SNP allele frequencies approach 0 in both scenarios. The cost of misclassifying the more common homozygote to the heterozygote also becomes indefinitely large as the minor SNP allele frequency goes to 0 under both scenarios. For the violation of HWE modeled here, the cost of misclassifying a heterozygote to the less common homozygote becomes large, although bounded. Therefore, the use of SNPs with a small minor allele frequency requires careful attention to the frequency of genotyping errors to ensure that power specifications are met. Furthermore, the design of automated genotyping should minimize those errors whose cost coefficients can become indefinitely large.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14748013 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.14748013eDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA. ~?gKarlin, S. Piazza, A.1981PStatistical methods for assessing linkage disequilibrium at the HLA-A, B, C loci79-94 Ann Hum Genet45Pt 1Alleles Chromosome Mapping HLA Antigens/*genetics HLA-A Antigens HLA-B Antigens HLA-C Antigens Haploidy Humans *Linkage (Genetics) Models, GeneticFebehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6947712 o0003-4800 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.6947712 ~?hSKe, X. Durrant, C. Morris, A. P. Hunt, S. Bentley, D. R. Deloukas, P. Cardon, L. R.2004UEfficiency and consistency of haplotype tagging of dense SNP maps in multiple samples2557-65 Hum Mol Genet1321African Americans/genetics *Chromosome Mapping Chromosomes, Human, Pair 20 European Continental Ancestry Group/genetics Gene Frequency Genetic Markers Great Britain *Haplotypes Humans Linkage Disequilibrium Mathematics *Polymorphism, Single NucleotideNov 1Haplotype tagging is a means of retaining most of the information in high density marker maps, while reducing genotyping requirements. Estimates of the numbers of tagging SNPs required to cover the human genome have varied widely, ranging from 100,000 to 1,000,000. Tagging has been applied to a number of gene-based datasets but has not been evaluated in contexts reflecting those of genome-wide association studies--large chromosome regions and multiple samples drawn from the same population. We analysed 5000 common markers across a 10 Mb segment of human chromosome 20 in three samples (UK Caucasian, CEPH Caucasian, African American) to evaluate tagging efficiency and consistency. Overall, the results indicate a high degree of efficiency, yielding 3-5-fold savings in Caucasians and 2-3-fold savings in African Americans. These levels varied according to linkage disequilibrium (LD) levels, tagging thresholds and allele frequencies, but in high LD regions they did not vary markedly due to marker density. However, a strong positive relationship between marker density and tagging was observed, relating to the fact that increasing marker density yields greater sequence coverage in high LD, thus requiring more tag SNPs to cover a greater fraction of the genome. Encouragingly, whatever the density employed, a high level of robustness was observed between UK and CEPH samples, as most of the htSNPs selected in one sample were also appropriate as tags in the other.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15367493 y0964-6906 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15367493eWellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK. J~?iKe, X. Hunt, S. Tapper, W. Lawrence, R. Stavrides, G. Ghori, J. Whittaker, P. Collins, A. Morris, A. P. Bentley, D. Cardon, L. R. Deloukas, P.2004JThe impact of SNP density on fine-scale patterns of linkage disequilibrium577-88 Hum Mol Genet136African Americans/genetics Algorithms Asian Continental Ancestry Group/genetics Chromosome Mapping European Continental Ancestry Group/genetics Gene Frequency *Genome, Human Genotype Great Britain Humans *Linkage Disequilibrium *Models, Genetic Polymorphism, Single Nucleotide/*geneticsMar 15Linkage disequilibrium (LD) is a measure of the degree of association between alleles in a population. The detection of disease-causing variants by association with neighbouring single nucleotide polymorphisms (SNPs) depends on the existence of strong LD between them. Previous studies have indicated that the extent of LD is highly variable in different chromosome regions and different populations, demonstrating the importance of genome-wide accurate measurement of LD at high resolution throughout the human genome. A uniform feature of these studies has been the inability to detect LD in regions of low marker density. To investigate the dependence of LD patterns on marker selection we performed a high-resolution study in African-American, Asian and UK Caucasian populations. We selected over 5000 SNPs with an average spacing of approximately 1 SNP per 2 kb after validating ca 12 000 SNPs derived from a dense SNP collection (1 SNP per 0.3 kb on average). Applications of different statistical methods of LD assessment highlight similar areas of high and low LD. However, at high resolution, features such as overall sequence coverage in LD blocks and block boundaries vary substantially with respect to marker density. Model-based linkage disequilibrium unit (LDU) maps appear robust to marker density and consistently influenced by marker allele frequency. The results suggest that very dense marker sets will be required to yield stable views of fine-scale LD in the human genome.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14734624 y0964-6906 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.14734624CWellcome Trust Centre for Human Genetics, University of Oxford, UK. ~?j_Ke, X. Miretti, M. M. Broxholme, J. Hunt, S. Beck, S. Bentley, D. R. Deloukas, P. Cardon, L. R.20057A comparison of tagging methods and their tagging space2757-67 Hum Mol Genet1418Algorithms Databases, Genetic Gene Frequency Genetic Markers/genetics *Genetic Techniques Genotype Haplotypes/genetics Polymorphism, Single Nucleotide/*genetics *Sequence Tagged SitesSep 15Single-nucleotide polymorphism (SNP) tagging is widely used as a way of saving genotyping costs in association studies. A number of different tagging methods have been developed to reduce the number of markers to be genotyped while maintaining power for detecting effects on non-assayed SNPs. How the different methods perform in different settings, the degree to which they overlap and share common tags and how they differ are important questions. We investigated these questions by comparing three widely used tagging methods/algorithms--one haplotype r2-based method, one pair-wise r2-based method and one method which was based on haplotype diversity but focused on major haplotypes. Tagging efficiency was defined as the number of genotyped markers divided by the number of tagging SNPs. Tagging effectiveness was defined as the proportion of un-genotyped or 'hidden' SNPs being detected (having a pair-wise or haplotype r2 with a set of tagging SNPs over a threshold, e.g. haplotype r2> or =0.80). The ENCODE regions genotyped on the HapMap CEPH individuals were examined in this study. Tagging effectiveness was generally poor for rare SNPs than for common SNPs, for all three tagging methods. Inclusion of rare SNPs into initial HapMap scheme could enhance the performance of tags on rare hidden SNPs at the expense of increased genotyping cost. At a moderate tagging efficiency, more than 90% of hidden SNPs detected by tagging SNPs selected by one method were also detected by tagging SNPs selected by another method, and this figure could be increased to 100% if tagging efficiency was allowed to drop. These results indicate that the tagging space is highly concordant between different tagging methods, despite the fact that they often involve different sets of tagging SNPs.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16103130 0964-6906 (Print) Comparative Study Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.16103130WWellcome Trust Centre for Human Genetics, University of Oxford, UK. xiayi@well.ox.ac.uk ~?khKeavney, B. McKenzie, C. A. Connell, J. M. Julier, C. Ratcliffe, P. J. Sobel, E. Lathrop, M. Farrall, M.1998GMeasured haplotype analysis of the angiotensin-I converting enzyme gene1745-51 Hum Mol Genet711England European Continental Ancestry Group/genetics Evolution, Molecular Genetic Techniques Haplotypes/*genetics Humans Peptidyl-Dipeptidase A/blood/*genetics *Polymorphism, Genetic Quantitative Trait, Heritable *Variation (Genetics)Oct~Linkage and segregation analysis have shown that circulating angiotensin-I converting enzyme (ACE) levels are influenced by a major quantitative trait locus that maps within or close to the ACE gene. The D variant of a 287 bp insertion/deletion (I/D) polymorphism in intron 16 of the gene is associated with high ACE levels and may also be related to increased risk of cardiovascular disease. Multiple variants that are in linkage disequilibrium with the I/D polymorphism have been described, but it is unknown if any of these are directly implicated, alone or in combination with as yet undiscovered variants, in the determination of ACE levels. An analysis of 10 polymorphisms spanning 26 kb of the ACE gene revealed a limited number of haplotypes in Caucasian British families due to strong linkage disequilibrium operating over this small chromosomal region. A haplotype tree (cladogram) was constructed with three main branches (clades A-C) which account for 90% of the observed haplotypes. Clade C is most likely derived from clades A and B following an ancestral recombination event. This evolutionary information was then used to direct a series of nested, measured haplotype analyses that excluded upstream sequences, including the ACE promoter, from harbouring the major ACE-linked variant that explains 36% of the total trait variability. Residual familial correlations were highly significant, suggesting the influence of additional unlinked genes. Our results demonstrate that a combined cladistic/measured haplotype analysis of polymorphisms within a gene provides a powerful means to localize variants that directly influence a quantitative trait.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9736776 B0964-6906 (Print) Journal Article Research Support, Non-U.S. Gov't9736776The Wellcome Trust Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Windmill Road, Oxford OX3 7BN, UK.~?lKempthorne, O. Osborne, R. H.1961The interpretation of twin data320-39Am J Hum Genet13*TwinsSepfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=13752449 !0002-9297 (Print) Journal Article13752449?mKendall, M. Stuart, A.1979FThe Advanced Theory of Statistics, Vol 2: Inference and Relationship. New York, NYJohn Wiley & Sons;~?nKent, J W Dyer, T D Blangero, J2005:Estimating the additive genetic effect of the X chromosome377-88Genet Epidemiol294Alleles *Chromosomes, Human, X *Dosage Compensation, Genetic Female Humans Linkage (Genetics) Male Quantitative Trait Loci Research Support, N.I.H., ExtramuralDecWe propose a method for efficient estimation of the additive genetic effect of the X chromosome with explicit modeling of eutherian-type dosage compensation. The theoretical derivation of the variance-components model for X-linked loci is reviewed in detail. We develop a model of dosage compensation that allows for both incomplete and heterogeneous lyonization, the existence of which is suggested by recent expression studies. Modeling this relationship, especially in the limit cases of complete or absent compensation, allows estimation of the X effect as a single parameter for ease of comparison to other sources of variance. We present simulation studies to estimate the power and computational efficiency of our proposed method.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16294301 !0741-0395 (Print) Journal Article16294301Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas 78245-0549, USA. jkent@darwin.sfbr.org<~?oPKent, J. W., Jr. Lease, L. R. Mahaney, M. C. Dyer, T. D. Almasy, L. Blangero, J.2005FX chromosome effects and their interactions with mitochondrial effectsS157 BMC Genet 6 Suppl 1Dec 30KABSTRACT : We report a simple and rapid method for detecting additive genetic variance due to X-linked loci in the absence of marker data for this chromosome. We examined the interaction of this method with an established method for detecting mitochondrial linkage (another source of sex-asymmetric genetic covariance). When applied to data from the Collaborative Study on the Genetics of Alcoholism, this method found evidence of X-chromosomal linkage for one continuous trait (ntth1) and one discrete trait (SPENT). Evidence of mitochondrial contribution was found for one discrete trait (CRAVING) and three continuous traits (ln(CIGPKYR), ecb21, and tth1). Results for ntth1 suggest that methods that do not also allow for male-female heterogeneity in environmental variance may be overly conservative in detection of X-chromosomal effects.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16451618 &1471-2156 (Electronic) Journal article16451618xDepartment of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX 78245, USA. jkent@darwin.sfbr.org.~?pkKerem, B. Rommens, J. M. Buchanan, J. A. Markiewicz, D. Cox, T. K. Chakravarti, A. Buchwald, M. Tsui, L. C.1989<Identification of the cystic fibrosis gene: genetic analysis1073-80Science2454922Alleles Chromosome Deletion Cystic Fibrosis/diagnosis/enzymology/*genetics DNA Mutational Analysis *Genes, Recessive Genetic Markers Haplotypes Humans Linkage (Genetics) Pancreas/enzymology Polymorphism, Restriction Fragment LengthSep 8Approximately 70 percent of the mutations in cystic fibrosis patients correspond to a specific deletion of three base pairs, which results in the loss of a phenylalanine residue at amino acid position 508 of the putative product of the cystic fibrosis gene. Extended haplotype data based on DNA markers closely linked to the putative disease gene locus suggest that the remainder of the cystic fibrosis mutant gene pool consists of multiple, different mutations. A small set of these latter mutant alleles (about 8 percent) may confer residual pancreatic exocrine function in a subgroup of patients who are pancreatic sufficient. The ability to detect mutations in the cystic fibrosis gene at the DNA level has important implications for genetic diagnosis.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2570460 g0036-8075 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.2570460MDepartment of Genetics, Hospital for Sick Children, Toronto, Ontario, Canada.~?q3Kidd, K. K. Pakstis, A. J. Speed, W. C. Kidd, J. R.2004*Understanding human DNA sequence variation406-20J Hered955*Base Sequence Demography Gene Frequency Genetics/history/*trends *Genetics, Population Geography Haplotypes/genetics Heterozygote Detection History, 20th Century History, 21st Century Humans *Models, Biological Polymorphism, Genetic *Variation (Genetics)Sep-OctOver the past century researchers have identified normal genetic variation and studied that variation in diverse human populations to determine the amounts and distributions of that variation. That information is being used to develop an understanding of the demographic histories of the different populations and the species as a whole, among other studies. With the advent of DNA-based markers in the last quarter century, these studies have accelerated. One of the challenges for the next century is to understand that variation. One component of that understanding will be population genetics. We present here examples of many of the ways these new data can be analyzed from a population perspective using results from our laboratory on multiple individual DNA-based polymorphisms, many clustered in haplotypes, studied in multiple populations representing all major geographic regions of the world. These data support an "out of Africa" hypothesis for human dispersal around the world and begin to refine the understanding of population structures and genetic relationships. We are also developing baseline information against which we can compare findings at different loci to aid in the identification of loci subject, now and in the past, to selection (directional or balancing). We do not yet have a comprehensive understanding of the extensive variation in the human genome, but some of that understanding is coming from population genetics.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15388768 0022-1503 (Print) Historical Article Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Review15388768Department of Genetics, Yale University School of Medicine, 333 Cedar St., New Haven, CT 06520-8005, USA. Kenneth.Kidd@yale.edu~?rKlein, R. J. Zeiss, C. Chew, E. Y. Tsai, J. Y. Sackler, R. S. Haynes, C. Henning, A. K. SanGiovanni, J. P. Mane, S. M. Mayne, S. T. Bracken, M. B. Ferris, F. L. Ott, J. Barnstable, C. Hoh, J.2005DComplement factor H polymorphism in age-related macular degeneration385-9Science3085720BAged Aged, 80 and over Aging Alleles Amino Acid Substitution Case-Control Studies Choroid/immunology Chromosomes, Human, Pair 1/genetics Complement Factor H/chemistry/*genetics/physiology Complement Membrane Attack Complex/analysis Exons Female Genetic Markers Genetic Predisposition to Disease Genotype Haplotypes Histidine/genetics Humans Immunity, Natural Introns Linkage Disequilibrium Macular Degeneration/*genetics Male Oligonucleotide Array Sequence Analysis Pigment Epithelium of Eye/immunology Polymorphism, Genetic *Polymorphism, Single Nucleotide Risk Factors SmokingApr 15BAge-related macular degeneration (AMD) is a major cause of blindness in the elderly. We report a genome-wide screen of 96 cases and 50 controls for polymorphisms associated with AMD. Among 116,204 single-nucleotide polymorphisms genotyped, an intronic and common variant in the complement factor H gene (CFH) is strongly associated with AMD (nominal P value <10(-7)). In individuals homozygous for the risk allele, the likelihood of AMD is increased by a factor of 7.4 (95% confidence interval 2.9 to 19). Resequencing revealed a polymorphism in linkage disequilibrium with the risk allele representing a tyrosine-histidine change at amino acid 402. This polymorphism is in a region of CFH that binds heparin and C-reactive protein. The CFH gene is located on chromosome 1 in a region repeatedly linked to AMD in family-based studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15761122 l1095-9203 (Electronic) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15761122fLaboratory of Statistical Genetics, Rockefeller University, 1230 York Avenue, New York, NY 10021, USA.?s Knapp, M.19972The affected sib pair method for linkage analysis.150-151!Genetic Mapping of Disease Genes.+I.H. Pawlowitzki J.H. Edwards E.A. ThompsonLondonAcademic Pressi~?t Knapp, M.1999GA note on power approximations for the transmission/disequilibrium test1177-85Am J Hum Genet644qAlleles Bipolar Disorder/genetics Chromosome Mapping/methods/*statistics & numerical data Computer Simulation Female Gene Frequency Genetic Predisposition to Disease/genetics Genotype Humans Likelihood Functions Linkage Disequilibrium/*genetics Male Matched-Pair Analysis Mathematics Models, Genetic Nuclear Family Odds Ratio Sample Size Tryptophan Hydroxylase/geneticsApr_The transmission/disequilibrium test (TDT) is a popular method for detection of the genetic basis of a disease. Investigators planning such studies require computation of sample size and power, allowing for a general genetic model. Here, a rigorous method is presented for obtaining the power approximations of the TDT for samples consisting of families with either a single affected child or affected sib pairs. Power calculations based on simulation show that these approximations are quite precise. By this method, it is also shown that a previously published power approximation of the TDT is erroneous.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10090903 !0002-9297 (Print) Journal Article10090903fInstitute for Medical Statistics, University of Bonn, 53105 Bonn, Germany. knapp@imsdd.meb.uni-bonn.dex~?u%Knapp, M. Seuchter, S. A. Baur, M. P.1994lLinkage analysis in nuclear families. 2: Relationship between affected sib-pair tests and lod score analysis44-51 Hum Hered441z*Data Interpretation, Statistical Genetic Diseases, Inborn/*genetics Genetic Markers Humans *Linkage (Genetics) *Lod ScoreJan-Feb"It is believed that the main advantage of affected sib-pair tests is that their application requires no information about the underlying genetic mechanism of the disease. However, here it is proved that the mean test, which can be considered the most prominent of the affected sib-pair tests, is equivalent to lod score analysis for an assumed recessive mode of inheritance, irrespective of the true mode of the disease. Further relationships of certain sib-pair tests and lod score analysis under specific assumed genetic modes are investigated.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8163291 T0001-5652 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't8163291:Institute for Medical Statistics, University of Bonn, FRG. ~?vKnapp, M. Strauch, K.2004Affected-sib-pair test for linkage based on constraints for identical-by-descent distributions corresponding to disease models with imprinting273-85Genet Epidemiol264Alleles *Genetic Heterogeneity *Genetic Predisposition to Disease Genotype Humans Likelihood Functions *Linkage (Genetics) Lod Score *Models, Genetic Models, StatisticalMayHolmans' possible triangle test for affected sib pairs has proven to be a powerful tool for linkage analysis. This test is a likelihood-ratio test for which maximization is restricted to the set of possible sharing probabilities. Here, we extend the possible triangle test to take into account genomic imprinting, which is also known as parent-of-origin effect. While the classical test without imprinting looks at whether affected sib pairs share 0, 1, or 2 alleles identical-by-descent, the likelihood-ratio test allowing for imprinting further distinguishes whether the sharing of exactly one allele is through the father or mother. Thus, if the disease gene is indeed subject to imprinting, the extended test presented here can take into account that affecteds will have inherited the mutant allele preferentially from one particular parent. We calculate the sharing probabilities at a marker locus linked to a disease susceptibility locus. Using our formulation, the constraints on these probabilities given by Dudoit and Speed ([1999] Statistics in Genetics; New York: Springer) can easily be verified. Next, we derive the asymptotic distribution of the restricted likelihood-ratio test statistic under the null hypothesis of no linkage, and give LOD-score criteria for various test sizes. We show, for various disease models, that the test allowing for imprinting has significantly higher power to detect linkage if imprinting is indeed present, at the cost of only a small reduction in power in case of no imprinting. Altogether, unlike many methods currently available, our novel model-free sib-pair test adequately models the epigenetic parent-of-origin effect, and will hopefully prove to be a useful tool for the genetic mapping of complex traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15095387 B0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't15095387sInstitute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, Bonn, Germany. knapp@uni-bonn.der~?wKnorr-Held, L. Rasser, G.2000BBayesian detection of clusters and discontinuities in disease maps13-21 Biometrics561*Bayes Theorem Biometry Cluster Analysis *Epidemiologic Methods Germany/epidemiology Humans Male Models, Statistical Mortality Mouth Neoplasms/epidemiologyMarAn interesting epidemiological problem is the analysis of geographical variation in rates of disease incidence or mortality. One goal of such an analysis is to detect clusters of elevated (or lowered) risk in order to identify unknown risk factors regarding the disease. We propose a nonparametric Bayesian approach for the detection of such clusters based on Green's (1995, Biometrika 82, 711-732) reversible jump MCMC methodology. The prior model assumes that geographical regions can be combined in clusters with constant relative risk within a cluster. The number of clusters, the location of the clusters, and the risk within each cluster is unknown. This specification can be seen as a change-point problem of variable dimension in irregular, discrete space. We illustrate our method through an analysis of oral cavity cancer mortality rates in Germany and compare the results with those obtained by the commonly used Bayesian disease mapping method of Besag, York, and Mollie (1991, Annals of the Institute of Statistical Mathematics, 43, 1-59).fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10783772 T0006-341X (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't10783772PInstitute of Statistics, University of Munich, Germany. leo@stat.uni-muenchen.de~?x>Knowler, W. C. Williams, R. C. Pettitt, D. J. Steinberg, A. G.1988cGm3;5,13,14 and type 2 diabetes mellitus: an association in American Indians with genetic admixture520-6Am J Hum Genet434Arizona Diabetes Mellitus, Type 2/epidemiology/*genetics European Continental Ancestry Group/genetics Genetic Markers Genetics, Population Haplotypes Humans Immunoglobulin Gm Allotypes/*genetics Indians, North American/*genetics Risk FactorsOctIn a sample of 4,920 Native Americans of the Pima and Papago tribes, there is a very strong negative association between the Gm haplotype Gm3;5,13,14 and type 2--or non-insulin-dependent--diabetes mellitus (prevalence ratio = 0.27, 95% confidence interval 0.18-0.40). One might conclude from this observation that the absence of this haplotype--or the presence of a closely linked gene--is a causal risk factor for the disease. It is shown that Gm3;5,13,14 is a marker for Caucasian admixture, and it is most likely the presence of Caucasian alleles and the concomitant decrease of Indian alleles that lowers the risk for diabetes, rather than the direct action of the haplotype or of a closely linked locus. This study demonstrates both the potential confounding effect of admixture on the interpretation of disease association studies and the importance of considering genetic admixture (or excluding individuals with genetic admixture) in studies of genetic markers of disease. The relationship between this admixture marker and the prevalence of diabetes also suggests a strong genetic component in the susceptibility to type 2 diabetes in Pima and Papago Indians.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3177389 !0002-9297 (Print) Journal Article3177389Diabetes and Arthritis Epidemiology Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ 85014.k~?yQKoenig, M. Hoffman, E. P. Bertelson, C. J. Monaco, A. P. Feener, C. Kunkel, L. M.1987Complete cloning of the Duchenne muscular dystrophy (DMD) cDNA and preliminary genomic organization of the DMD gene in normal and affected individuals509-17Cell503Amino Acid Sequence Base Sequence Chromosome Deletion Chromosome Mapping Cloning, Molecular Dna *Gene Expression Regulation Humans Muscular Dystrophies/*geneticsJul 31The 14 kb human Duchenne muscular dystrophy (DMD) cDNA corresponding to a complete representation of the fetal skeletal muscle transcript has been cloned. The DMD transcript is formed by at least 60 exons which have been mapped relative to various reference points within Xp21. The first half of the DMD transcript is formed by a minimum of 33 exons spanning nearly 1000 kb, and the remaining portion has at least 27 exons that may spread over a similar distance. The DNA isolated from 104 DMD boys was tested with the cDNA for detection of deletions and 53 patients exhibit deletion mutations. The majority of deletions are concentrated in a single genomic segment corresponding to only 2 kb of the transcript.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3607877 g0092-8674 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.3607877~?zKong, A. Cox, N. J.1997<Allele-sharing models: LOD scores and accurate linkage tests1179-88Am J Hum Genet615*Alleles Chromosomes, Human, Pair 2/genetics Computer Simulation Diabetes Mellitus, Type 2/genetics Genetic Markers/genetics Humans Linkage (Genetics)/*genetics *Lod Score Mathematics *Models, Genetic SoftwareNovStarting with a test statistic for linkage analysis based on allele sharing, we propose an associated one-parameter model. Under general missing-data patterns, this model allows exact calculation of likelihood ratios and LOD scores and has been implemented by a simple modification of existing software. Most important, accurate linkage tests can be performed. Using an example, we show that some previously suggested approaches to handling less than perfectly informative data can be unacceptably conservative. Situations in which this model may not perform well are discussed, and an alternative model that requires additional computations is suggested.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9345087 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9345087`Department of Statistics, University of Chicago, Chicago, IL 60637, US. kong@galton.uchicago.edu~?{Kong, A. Gudbjartsson, D. F. Sainz, J. Jonsdottir, G. M. Gudjonsson, S. A. Richardsson, B. Sigurdardottir, S. Barnard, J. Hallbeck, B. Masson, G. Shlien, A. Palsson, S. T. Frigge, M. L. Thorgeirsson, T. E. Gulcher, J. R. Stefansson, K.20027A high-resolution recombination map of the human genome241-7 Nat Genet313Base Sequence Chromosome Banding *Chromosome Mapping *Genome, Human Genotype Humans Meiosis Microsatellite Repeats/*genetics Pedigree Polymorphism, Single Nucleotide Recombination, Genetic/*genetics Regression AnalysisJulVDetermination of recombination rates across the human genome has been constrained by the limited resolution and accuracy of existing genetic maps and the draft genome sequence. We have genotyped 5,136 microsatellite markers for 146 families, with a total of 1,257 meiotic events, to build a high-resolution genetic map meant to: (i) improve the genetic order of polymorphic markers; (ii) improve the precision of estimates of genetic distances; (iii) correct portions of the sequence assembly and SNP map of the human genome; and (iv) build a map of recombination rates. Recombination rates are significantly correlated with both cytogenetic structures (staining intensity of G bands) and sequence (GC content, CpG motifs and poly(A)/poly(T) stretches). Maternal and paternal chromosomes show many differences in locations of recombination maxima. We detected systematic differences in recombination rates between mothers and between gametes from the same mother, suggesting that there is some underlying component determined by both genetic and environmental factors that affects maternal recombination rates.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12053178 31061-4036 (Print) Comparative Study Journal Article12053178HdeCODE genetics, Sturlugotu 8, IS-101 Reykjavik, Iceland. kong@decode.isw~?|=Kong, X. Murphy, K. Raj, T. He, C. White, P. S. Matise, T. C.20043A combined linkage-physical map of the human genome1143-8Am J Hum Genet756*Chromosome Mapping *Databases, Genetic Genetic Markers/genetics *Genome, Human Humans *Physical Chromosome Mapping *Polymorphism, GeneticDecWe have constructed de novo a high-resolution genetic map that includes the largest set, to our knowledge, of polymorphic markers (N=14,759) for which genotype data are publicly available; that combines genotype data from both the Centre d'Etude du Polymorphisme Humain (CEPH) and deCODE pedigrees; that incorporates single-nucleotide polymorphisms; and that also incorporates sequence-based positional information. The position of all markers on our map is corroborated by both genomic sequence and recombination-based data. This specific combination of features maximizes marker inclusion, coverage, and resolution, making this map uniquely suitable as a comprehensive resource for determining genetic map information (order and distances) for any large set of polymorphic markers.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15486828 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15486828FDepartment of Genetics, Rutgers University, Piscataway, NJ 08840, USA.?} Kraemer, H.C1985NA strategy to teach the concept and application of power of statistical tests.173-195J. Educat. Stat. 10 ~?~:Kraft, P. Cox, D. G. Paynter, R. A. Hunter, D. De Vivo, I.2005sAccounting for haplotype uncertainty in matched association studies: a comparison of simple and flexible techniques261-72Genet Epidemiol283Algorithms Alleles Bayes Theorem Case-Control Studies Computer Simulation Endometrial Neoplasms/*genetics Female Gene Frequency Genetic Predisposition to Disease Genotype Haplotypes/*genetics Humans Logistic Models *Models, Genetic Polymorphism, Single NucleotideAprPopulation-based case-control studies measuring associations between haplotypes of single nucleotide polymorphisms (SNPs) are increasingly popular, in part because haplotypes of a few "tagging" SNPs may serve as surrogates for variation in relatively large sections of the genome. Due to current technological limitations, haplotypes in cases and controls must be inferred from unphased genotypic data. Using individual-specific inferred haplotypes as covariates in standard epidemiologic analyses (e.g., conditional logistic regression) is an attractive analysis strategy, as it allows adjustment for nongenetic covariates, provides omnibus and haplotype-specific tests of association, and can estimate haplotype and haplotype x environment interaction effects. In principle, some adjustment for the uncertainty in inferred haplotypes should be made. Via simulation, we compare the performance (bias and mean squared error of haplotype and haplotype x environment interaction effect estimates) of several analytic strategies using inferred haplotypes in the context of matched case-control data. These strategies include using only the most likely haplotype assignment, the expectation substitution approach described by Stram et al. ([2003b] Hum. Hered. 55:179-190) and others, and an improper version of multiple imputation. For relatively uncomplicated haplotype structures and moderate haplotype relative risks (/=5). An application to progesterone-receptor haplotypes and endometrial cancer further illustrates that the performance of all these methods depends on how well the observed haplotypes "tag" the unobserved causal variant.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15637718 !0741-0395 (Print) Journal Article15637718pDepartment of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. pkraft@hsph.harvard.edu~? Kruglyak, L.1997@The use of a genetic map of biallelic markers in linkage studies21-4 Nat Genet171Alleles Chromosome Mapping/*methods *Genetic Markers Genetics, Medical/*methods Genotype Humans *Linkage (Genetics) Lod Score Microsatellite Repeats Polymorphism, GeneticSepCImprovements in genetic mapping techniques have driven recent progress in human genetics. The use of single nucleotide polymorphisms (SNPs) as biallelic genetic markers offers the promise of rapid, highly automated genotyping. As maps of SNPs and the techniques for genotyping them are being developed, it is important to consider what properties such maps must have in order for them to be useful for linkage studies. I examine how polymorphic and densely spaced biallelic markers need to be for extraction of most of the inheritance information from human pedigrees, and compare maps of biallelics with today's genome-scanning sets of microsatellite markers. I conclude that a map of 700-900 moderately polymorphic biallelic markers is equivalent--and a map of 1,500-3,000 superior--to the current 300-400 microsatellite marker sets.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9288093 X1061-4036 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.9288093jWhitehead Institute for Biomedical Research, Cambridge, Massachusetts 02139, USA. leonid@genome.wi.mit.edu\~? Kruglyak, L.1999QProspects for whole-genome linkage disequilibrium mapping of common disease genes139-44 Nat Genet222*Chromosome Mapping Gene Frequency Genetic Diseases, Inborn/*genetics Genetic Markers *Genome, Human Humans *Linkage Disequilibrium Models, Genetic Polymorphism, Genetic Variation (Genetics)JunRecently, attention has focused on the use of whole-genome linkage disequilibrium (LD) studies to map common disease genes. Such studies would employ a dense map of single nucleotide polymorphisms (SNPs) to detect association between a marker and disease. Construction of SNP maps is currently underway. An essential issue yet to be settled is the required marker density of such maps. Here, I use population simulations to estimate the extent of LD surrounding common gene variants in the general human population as well as in isolated populations. Two main conclusions emerge from these investigations. First, a useful level of LD is unlikely to extend beyond an average distance of roughly 3 kb in the general population, which implies that approximately 500,000 SNPs will be required for whole-genome studies. Second, the extent of LD is similar in isolated populations unless the founding bottleneck is very narrow or the frequency of the variant is low (<5%).fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10369254 g1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.10369254XFred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA. leonid@fhcrc.orgS~?Kruglyak, L. Daly, M. J.1998-Linkage thresholds for two-stage genome scans994-7Am J Hum Genet624>*Computer Simulation *Genome, Human Humans *Linkage (Genetics)Aprehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9529350 0002-9297 (Print) Letter9529350 ~?8Kruglyak, L. Daly, M. J. Reeve-Daly, M. P. Lander, E. S.1996LParametric and nonparametric linkage analysis: a unified multipoint approach1347-63Am J Hum Genet586Algorithms Female Genes, Dominant Genetic Diseases, Inborn/*genetics Haplotypes Humans *Linkage (Genetics) Lod Score Male *Models, Genetic *Models, Statistical *Pedigree Reproducibility of Results Schizophrenia/*genetics Software Statistics, NonparametricJunIn complex disease studies, it is crucial to perform multipoint linkage analysis with many markers and to use robust nonparametric methods that take account of all pedigree information. Currently available methods fall short in both regards. In this paper, we describe how to extract complete multipoint inheritance information from general pedigrees of moderate size. This information is captured in the multipoint inheritance distribution, which provides a framework for a unified approach to both parametric and nonparametric methods of linkage analysis. Specifically, the approach includes the following: (1) Rapid exact computation of multipoint LOD scores involving dozens of highly polymorphic markers, even in the presence of loops and missing data. (2) Non-parametric linkage (NPL) analysis, a powerful new approach to pedigree analysis. We show that NPL is robust to uncertainty about mode of inheritance, is much more powerful than commonly used nonparametric methods, and loses little power relative to parametric linkage analysis. NPL thus appears to be the method of choice for pedigree studies of complex traits. (3) Information-content mapping, which measures the fraction of the total inheritance information extracted by the available marker data and points out the regions in which typing additional markers is most useful. (4) Maximum-likelihood reconstruction of many-marker haplotypes, even in pedigrees with missing data. We have implemented NPL analysis, LOD-score computation, information-content mapping, and haplotype reconstruction in a new computer package, GENEHUNTER. The package allows efficient multipoint analysis of pedigree data to be performed rapidly in a single user-friendly environment.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8651312 X0002-9297 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.8651312PWhitehead Institute for Biomedical Research, Cambridge. leonid@genome.wi.mit.edu~?Kruglyak, L. Lander, E. S.1995LComplete multipoint sib-pair analysis of qualitative and quantitative traits439-54Am J Hum Genet572Chromosome Mapping Diabetes Mellitus, Type 1/genetics Genetic Diseases, Inborn/*genetics Genetic Markers Heterozygote Humans *Linkage (Genetics) *Models, Genetic *Models, Statistical SoftwareAug/Sib-pair analysis is an increasingly important tool for genetic dissection of complex traits. Current methods for sib-pair analysis are primarily based on studying individual genetic markers one at a time and thus fail to use the full inheritance information provided by multipoint linkage analysis. In this paper, we describe how to extract the complete multipoint inheritance information for each sib pair. We then describe methods that use this information to map loci affecting traits, thereby providing a unified approach to both qualitative and quantitative traits. Specifically, complete multipoint approaches are presented for (1) exclusion mapping of qualitative traits; (2) maximum-likelihood mapping of qualitative traits; (3) information-content mapping, showing the extent to which all inheritance information has been extracted at each location in the genome; and (4) quantitative-trait mapping, by two parametric methods and one nonparametric method. In addition, we explore the effects of marker density, marker polymorphism, and availability of parents on the information content of a study. We have implemented the analysis methods in a new computer package, MAPMAKER/SIBS. With this computer package, complete multipoint analysis with dozens of markers in hundreds of sib pairs can be carried out in minutes.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7668271 !0002-9297 (Print) Journal Article7668271KWhitehead Institute for Biomedical Research, Cambridge, MA 02142-1479, USA.~?Kruglyak, L. Lander, E. S.1998;Faster multipoint linkage analysis using Fourier transforms1-7 J Comput Biol51Algorithms Female *Fourier Analysis Genetic Markers/genetics Humans Linkage (Genetics)/*genetics Male Markov Chains Pedigree Software Werner Syndrome/geneticsSpringGenetic linkage analysis of human pedigrees using many linked markers simultaneously is a difficult computational problem. We have previously described an approach to this problem that uses hidden Markov models (HMMs) and is quite efficient for pedigrees of moderate size. Here, we describe a new, faster algorithm for the key step in the HMM calculation. The algorithm employs a fast Fourier transform on the group of pedigree inheritance patterns. It substantially improves the overall performance of the software package GENEHUNTER for performing linkage analysis. The Fourier representation opens up new research directions for pedigree analysis.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9541867 F1066-5277 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9541867FWhitehead Institute for Biomedical Research, Cambridge, MA 02142, USA. !~?MKupper, N. Willemsen, G. Riese, H. Posthuma, D. Boomsma, D. I. de Geus, E. J.2005LHeritability of daytime ambulatory blood pressure in an extended twin design80-5 Hypertension451uAdult Antihypertensive Agents/pharmacology/therapeutic use Blood Pressure/drug effects/*genetics Blood Pressure Monitoring, Ambulatory Circadian Rhythm Diastole Female Humans Hypertension/drug therapy/genetics/physiopathology Male Multifactorial Inheritance Multivariate Analysis Netherlands/epidemiology Research Design Siblings Systole Twins, Dizygotic Twins, MonozygoticJanfThe present study estimated the genetic influences on ambulatory systolic and diastolic blood pressure, and on hypertensive status derived from ambulatory levels, in a family sample of 535 twins and 257 singleton siblings. This "extended twin design" was used to explicitly test the possibility that results obtained in singleton siblings are different from those obtained in twins. To examine the effects of excluding (medicated) hypertensive subjects, the genetic analyses were first performed under strict exclusion (medication and/or blood pressure >135/85 mm Hg), then without the medicated subjects, and, finally, without any exclusion. For the latter analysis, the untreated blood pressure values in subjects using antihypertensive medication were estimated by augmenting the observed blood pressure by the published efficacy of the specific antihypertensive medication used. No evidence was found for differential means, variances, or covariances of ambulatory blood pressure in singletons compared with twins. This indicates that estimates of heritability of ambulatory blood pressure from twin studies can be generalized to the singleton population. Heritability of hypertension, defined as a mean daytime blood pressure >135/85 mm Hg or antihypertensive medication use, was 61%. Genetic contribution to ambulatory blood pressure was highest when all subjects were included (systolic, 44% to 57%; diastolic, 46% to 63%) and lowest under strict exclusion (systolic, 32% to 50%; diastolic, 31% to 55%). We conclude that exclusion of (medicated) hypertensives removes part of the true genetic variance in ambulatory blood pressure.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15557390 d1524-4563 (Electronic) Comparative Study Journal Article Research Support, Non-U.S. Gov't Twin Study15557390Department of Biological Psychology, Vrije Universiteit, van der Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands. hm.kupper@psy.vu.nlx~? Kwok, P. Y.20016Methods for genotyping single nucleotide polymorphisms235-58Annu Rev Genomics Hum Genet2JAlleles Genotype Humans *Polymorphism, Single Nucleotide Spectrum AnalysisOne of the fruits of the Human Genome Project is the discovery of millions of DNA sequence variants in the human genome. The majority of these variants are single nucleotide polymorphisms (SNPs). A dense set of SNP markers opens up the possibility of studying the genetic basis of complex diseases by population approaches. In all study designs, a large number of individuals must be genotyped with a large number of markers. In this review, the current status of SNP genotyping is discussed in terms of the mechanisms of allelic discrimination, the reaction formats, and the detection modalities. A number of genotyping methods currently in use are described to illustrate the approaches being taken. Although no single genotyping method is ideally suited for all applications, a number of good genotyping methods are available to meet the needs of many study designs. The challenges for SNP genotyping in the near future include increasing the speed of assay development, reducing the cost of the assays, and performing multiple assays in parallel. Judging from the accelerated pace of new method development, it is hopeful that an ideal SNP genotyping method will be developed soon.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11701650 (1527-8204 (Print) Journal Article Review11701650aWashington University School of Medicine, St. Louis, Missouri 63110, USA. kwok@genetics.wustl.edu ~?Laird, N. M. Horvath, S. Xu, X.2000DImplementing a unified approach to family-based tests of associationS36-42Genet Epidemiol 19 Suppl 1?Genotype Humans *Models, Genetic Phenotype Software *StatisticsWe describe a broad class of family-based association tests that are adjusted for admixture; use either dichotomous or measured phenotypes; accommodate phenotype-unknown subjects; use nuclear families, sibships or a combination of the two, permit multiple nuclear families from a single pedigree; incorporate di- or multi-allelic marker data; allow additive, dominant or recessive models; and permit adjustment for covariates and gene-by-environment interactions. The test statistic is basically the covariance between a user-specified function of the genotype and a user-specified function of the trait. The distribution of the statistic is computed using the appropriate conditional distribution of offspring genotypes that adjusts for admixture.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11055368 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11055368yDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. laird@biostat.harvard.edu~?Laird, N. M. Lange, C.2006GFamily-based designs in the age of large-scale gene-association studies385-94 Nat Rev Genet75Case-Control Studies *Family *Genetic Techniques Genetics, Population Genome, Human Haplotypes Humans Linkage Disequilibrium Models, Genetic Pedigree Phenotype Quantitative Trait, HeritableMay.Both population-based and family-based designs are commonly used in genetic association studies to locate genes that underlie complex diseases. The simplest version of the family-based design--the transmission disequilibrium test--is well known, but the numerous extensions that broaden its scope and power are less widely appreciated. Family-based designs have unique advantages over population-based designs, as they are robust against population admixture and stratification, allow both linkage and association to be tested for and offer a solution to the problem of model building. Furthermore, the fact that family-based designs contain both within- and between-family information has substantial benefits in terms of multiple-hypothesis testing, especially in the context of whole-genome association studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16619052 M1471-0056 (Print) Journal Article Research Support, N.I.H., Extramural Review16619052vDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. laird@hsph.harvard.edu ~?$Lake, S. L. Blacker, D. Laird, N. M.2000<Family-based tests of association in the presence of linkage1515-25Am J Hum Genet676HAlgorithms Alleles Alzheimer Disease/*genetics Chromosome Mapping/*methods/*statistics & numerical data Chromosomes, Human, Pair 12/genetics Exons/genetics Genetic Markers/genetics Genotype Humans Linkage (Genetics)/*genetics Models, Genetic Monte Carlo Method Nuclear Family RNA Splice Sites/genetics Sequence Deletion/geneticsDecRLinkage analysis may not provide the necessary resolution for identification of the genes underlying phenotypic variation. This is especially true for gene-mapping studies that focus on complex diseases that do not exhibit Mendelian inheritance patterns. One positional genomic strategy involves application of association methodology to areas of identified linkage. Detection of association in the presence of linkage localizes the gene(s) of interest to more-refined regions in the genome than is possible through linkage analysis alone. This strategy introduces a statistical complexity when family-based association tests are used: the marker genotypes among siblings are correlated in linked regions. Ignoring this correlation will compromise the size of the statistical hypothesis test, thus clouding the interpretation of test results. We present a method for computing the expectation of a wide range of association test statistics under the null hypothesis that there is linkage but no association. To standardize the test statistic, an empirical variance-covariance estimator that is robust to the sibling marker-genotype correlation is used. This method is widely applicable: any type of phenotypic measure or family configuration can be used. For example, we analyze a deletion in the A2M gene at the 5' splice site of "exon II" of the bait region in Alzheimer disease (AD) discordant sibships. Since the A2M gene lies in a chromosomal region (chromosome 12p) that consistently has been linked to AD, association tests should be conducted under the null hypothesis that there is linkage but no association.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11058432 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11058432Department of Biostatistics, Harvard School of Public Health, Harvard University, Boston, MA 02115, USA. slake@hsph.harvard.edu $~?Lamb, J. A. Barnby, G. Bonora, E. Sykes, N. Bacchelli, E. Blasi, F. Maestrini, E. Broxholme, J. Tzenova, J. Weeks, D. Bailey, A. J. Monaco, A. P.2005mAnalysis of IMGSAC autism susceptibility loci: evidence for sex limited and parent of origin specific effects132-7 J Med Genet422Autistic Disorder/*genetics Female *Genetic Predisposition to Disease Genomic Imprinting Humans Linkage (Genetics) Male Parents Sex Factors SiblingsFebBACKGROUND AND METHODS: Autism is a severe neurodevelopmental disorder, which has a complex genetic predisposition. The ratio of males to females affected by autism is approximately 4:1, suggesting that sex specific factors are involved in its development. We reported previously the results of a genomewide screen for autism susceptibility loci in 83 affected sibling pairs (ASP), and follow up analysis in 152 ASP. Here, we report analysis of an expanded sample of 219 ASP, using sex and parent of origin linkage modelling at loci on chromosomes 2, 7, 9, 15, and 16. RESULTS: The results suggest that linkage to chromosomes 7q and 16p is contributed largely by the male-male ASP (MLS = 2.55 v 0.12, and MLS = 2.48 v 0.00, for the 145 male-male and 74 male-female/female-female ASP on chromosomes 7 and 16 respectively). Conversely linkage to chromosome 15q appears to be attributable to the male-female/female-female ASP (MLS = 2.62 v 0.00, for non-male and male-male ASP respectively). On chromosomes 2 and 9, all ASP contribute to linkage. These data, supported by permutation, suggest a possible sex limited effect of susceptibility loci on chromosomes 7, 15, and 16. Parent of origin linkage modelling indicates two distinct regions of paternal and maternal identity by descent sharing on chromosome 7 (paternal MLS = 1.46 at approximately 112 cM, and maternal MLS = 1.83 at approximately 135 cM; corresponding maternal and paternal MLS = 0.53 and 0.28 respectively), and maternal specific sharing on chromosome 9 (maternal MLS = 1.99 at approximately 30 cM; paternal MLS = 0.02). CONCLUSION: These data support the possibility of two discrete loci underlying linkage of autism to chromosome 7, and implicate possible parent of origin specific effects in the aetiology of autism.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15689451 1468-6244 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15689451KWellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.~?Lander, E. Kruglyak, L.1995_Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results241-7 Nat Genet113nGenetics/*standards *Genome *Linkage (Genetics) Lod Score Models, Genetic Research Design/standards StatisticsNovGenetic studies are under way for many complex traits, spurred by the recent feasibility of whole genome scans. Clear guidelines for the interpretation of linkage results are needed to avoid a flood of false positive claims. At the same time, an overly cautious approach runs the risk of causing true hints of linkage to be missed. We address this problem by proposing specific standards designed to maintain rigor while also promoting communication.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7581446 F1061-4036 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.7581446PWhitehead Institute for Biomedical Research, Cambridge Massachusetts 02142, USA.b~?Lander, E. S. Botstein, D.1989PMapping mendelian factors underlying quantitative traits using RFLP linkage maps185-99Genetics1211*Chromosome Mapping Crosses, Genetic Environment Genetic Markers Genetic Techniques Inbreeding *Linkage (Genetics) Lod Score Mathematics *Polymorphism, Genetic *Polymorphism, Restriction Fragment Length Recombination, Genetic *Restriction MappingJanThe advent of complete genetic linkage maps consisting of codominant DNA markers [typically restriction fragment length polymorphisms (RFLPs)] has stimulated interest in the systematic genetic dissection of discrete Mendelian factors underlying quantitative traits in experimental organisms. We describe here a set of analytical methods that modify and extend the classical theory for mapping such quantitative trait loci (QTLs). These include: (i) a method of identifying promising crosses for QTL mapping by exploiting a classical formula of SEWALL WRIGHT; (ii) a method (interval mapping) for exploiting the full power of RFLP linkage maps by adapting the approach of LOD score analysis used in human genetics, to obtain accurate estimates of the genetic location and phenotypic effect of QTLs; and (iii) a method (selective genotyping) that allows a substantial reduction in the number of progeny that need to be scored with the DNA markers. In addition to the exposition of the methods, explicit graphs are provided that allow experimental geneticists to estimate, in any particular case, the number of progeny required to map QTLs underlying a quantitative trait.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2563713 k0016-6731 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.2563713LWhitehead Institute for Biomedical Research, Cambridge, Massachusetts 02142.~?Lander, E. S. Green, P.19879Construction of multilocus genetic linkage maps in humans2363-7Proc Natl Acad Sci U S A848Algorithms Chromosome Mapping Genetic Vectors Genotype Humans *Linkage (Genetics) *Models, Genetic Probability Recombination, GeneticAprHuman genetic linkage maps are most accurately constructed by using information from many loci simultaneously. Traditional methods for such multilocus linkage analysis are computationally prohibitive in general, even with supercomputers. The problem has acquired practical importance because of the current international collaboration aimed at constructing a complete human linkage map of DNA markers through the study of three-generation pedigrees. We describe here several alternative algorithms for constructing human linkage maps given a specified gene order. One method allows maximum-likelihood multilocus linkage maps for dozens of DNA markers in such three-generation pedigrees to be constructed in minutes.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3470801 k0027-8424 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.3470801~?l Lander, E. S. Linton, L. M. Birren, B. Nusbaum, C. Zody, M. C. Baldwin, J. Devon, K. Dewar, K. Doyle, M. FitzHugh, W. Funke, R. Gage, D. Harris, K. Heaford, A. Howland, J. Kann, L. Lehoczky, J. LeVine, R. McEwan, P. McKernan, K. Meldrim, J. Mesirov, J. P. Miranda, C. Morris, W. Naylor, J. Raymond, C. Rosetti, M. Santos, R. Sheridan, A. Sougnez, C. Stange-Thomann, N. Stojanovic, N. Subramanian, A. Wyman, D. Rogers, J. Sulston, J. Ainscough, R. Beck, S. Bentley, D. Burton, J. Clee, C. Carter, N. Coulson, A. Deadman, R. Deloukas, P. Dunham, A. Dunham, I. Durbin, R. French, L. Grafham, D. Gregory, S. Hubbard, T. Humphray, S. Hunt, A. Jones, M. Lloyd, C. McMurray, A. Matthews, L. Mercer, S. Milne, S. Mullikin, J. C. Mungall, A. Plumb, R. Ross, M. Shownkeen, R. Sims, S. Waterston, R. H. Wilson, R. K. Hillier, L. W. McPherson, J. D. Marra, M. A. Mardis, E. R. Fulton, L. A. Chinwalla, A. T. Pepin, K. H. Gish, W. R. Chissoe, S. L. Wendl, M. C. Delehaunty, K. D. Miner, T. L. Delehaunty, A. Kramer, J. B. Cook, L. L. Fulton, R. S. Johnson, D. L. Minx, P. J. Clifton, S. W. Hawkins, T. Branscomb, E. Predki, P. Richardson, P. Wenning, S. Slezak, T. Doggett, N. Cheng, J. F. Olsen, A. Lucas, S. Elkin, C. Uberbacher, E. Frazier, M. Gibbs, R. A. Muzny, D. M. Scherer, S. E. Bouck, J. B. Sodergren, E. J. Worley, K. C. Rives, C. M. Gorrell, J. H. Metzker, M. L. Naylor, S. L. Kucherlapati, R. S. Nelson, D. L. Weinstock, G. M. Sakaki, Y. Fujiyama, A. Hattori, M. Yada, T. Toyoda, A. Itoh, T. Kawagoe, C. Watanabe, H. Totoki, Y. Taylor, T. Weissenbach, J. Heilig, R. Saurin, W. Artiguenave, F. Brottier, P. Bruls, T. Pelletier, E. Robert, C. Wincker, P. Smith, D. R. Doucette-Stamm, L. Rubenfield, M. Weinstock, K. Lee, H. M. Dubois, J. Rosenthal, A. Platzer, M. Nyakatura, G. Taudien, S. Rump, A. Yang, H. Yu, J. Wang, J. Huang, G. Gu, J. Hood, L. Rowen, L. Madan, A. Qin, S. Davis, R. W. Federspiel, N. A. Abola, A. P. Proctor, M. J. Myers, R. M. Schmutz, J. Dickson, M. Grimwood, J. Cox, D. R. Olson, M. V. Kaul, R. Raymond, C. Shimizu, N. Kawasaki, K. Minoshima, S. Evans, G. A. Athanasiou, M. Schultz, R. Roe, B. A. Chen, F. Pan, H. Ramser, J. Lehrach, H. Reinhardt, R. McCombie, W. R. de la Bastide, M. Dedhia, N. Blocker, H. Hornischer, K. Nordsiek, G. Agarwala, R. Aravind, L. Bailey, J. A. Bateman, A. Batzoglou, S. Birney, E. Bork, P. Brown, D. G. Burge, C. B. Cerutti, L. Chen, H. C. Church, D. Clamp, M. Copley, R. R. Doerks, T. Eddy, S. R. Eichler, E. E. Furey, T. S. Galagan, J. Gilbert, J. G. Harmon, C. Hayashizaki, Y. Haussler, D. Hermjakob, H. Hokamp, K. Jang, W. Johnson, L. S. Jones, T. A. Kasif, S. Kaspryzk, A. Kennedy, S. Kent, W. J. Kitts, P. Koonin, E. V. Korf, I. Kulp, D. Lancet, D. Lowe, T. M. McLysaght, A. Mikkelsen, T. Moran, J. V. Mulder, N. Pollara, V. J. Ponting, C. P. Schuler, G. Schultz, J. Slater, G. Smit, A. F. Stupka, E. Szustakowski, J. Thierry-Mieg, D. Thierry-Mieg, J. Wagner, L. Wallis, J. Wheeler, R. Williams, A. Wolf, Y. I. Wolfe, K. H. Yang, S. P. Yeh, R. F. Collins, F. Guyer, M. S. Peterson, J. Felsenfeld, A. Wetterstrand, K. A. Patrinos, A. Morgan, M. J. de Jong, P. Catanese, J. J. Osoegawa, K. Shizuya, H. Choi, S. Chen, Y. J.20013Initial sequencing and analysis of the human genome860-921Nature4096822Animals Chromosome Mapping Conserved Sequence CpG Islands DNA Transposable Elements Databases, Factual Drug Industry Evolution, Molecular Forecasting GC Rich Sequence Gene Duplication Genes Genetic Diseases, Inborn Genetics, Medical *Genome, Human *Human Genome Project Humans Mutation Private Sector Proteins/genetics Proteome Public Sector RNA/genetics Repetitive Sequences, Nucleic Acid *Sequence Analysis, DNA/methods Species SpecificityFeb 15yThe human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11237011 0028-0836 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.11237011Whitehead Institute for Biomedical Research, Center for Genome Research, Cambridge, Massachusetts 02142, USA. lander@genome.wi.mit.edu~?>Lange, C. DeMeo, D. Silverman, E. K. Weiss, S. T. Laird, N. M.20040PBAT: tools for family-based association studies367-9Am J Hum Genet7423Genetic Diseases, Inborn/*genetics Humans *SoftwareFebfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14740322 =0002-9297 (Print) Letter Research Support, U.S. Gov't, P.H.S.14740322~?#Lange, C. DeMeo, D. L. Laird, N. M.2002jPower and design considerations for a general class of family-based association tests: quantitative traits1330-41Am J Hum Genet716Adult Asthma/*genetics Child Chromosome Mapping/*methods/statistics & numerical data Gene Frequency Humans *Nuclear Family Phenotype Quantitative Trait Loci/*genetics *Quantitative Trait, Heritable Sample SizeDecIn the present article, we address family-based association tests (FBATs) for quantitative traits. We propose an approach to analytical power and sample-size calculations for general FBATs; this approach can be applied to virtually any scenario (missing parental information, multiple offspring per family, etc.). The power calculations are used to discuss optimal choices of the phenotypes for the FBAT statistic and its power's dependence on ascertainment conditions, on study design, and on the correct specification of the distributional assumptions for the phenotypes. We also compare the general FBAT approach with PDT and QTDT. The practical relevance of our theoretical considerations is illustrated by their application to an asthma study.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12454799 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.12454799lDepartment of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. clange@hsph.harvard.eduw? Lange, K.19979Mathematical and Statistcal Methods for Genetic Analysis. New York, NY.Springer)~?Lange, K. Boehnke, M.1983PExtensions to pedigree analysis. V. Optimal calculation of Mendelian likelihoods291-301 Hum Hered335NABO Blood-Group System Genotype Humans Models, Theoretical *Pedigree Phenotype]Mendelian likelihoods are computed from human pedigree data for purposes of gene mapping, risk prediction in genetic counseling, and hypothesis testing in genetic epidemiology. The Mendelian likelihood of an extended pedigree can be written as a sum of products, the sum ranging over all possible genotypic combinations for the individuals in the pedigree. Exclusion of genotypes incompatible with the phenotypic information and pedigree structure reduces the ranges of summation and simplifies the likelihood calculation. To evaluate the likelihood with the fewest possible arithmetic operations requires carrying out the summations over one individual at a time and the intervening multiplications in some appropriate order. Each such removal of an individual reduces the likelihood evaluation to another evaluation of the same numerical form. Greedy-type algorithms are suggested for determining an order in which the summations and multiplications may be carried out. The greedy methods are fast and appear to generate good removal sequences. They are shown to work well when applied to a large, complex pedigree.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6654362 g0001-5652 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.6654362R~?Lange, K. Goradia, T. M.1987/An algorithm for automatic genotype elimination250-6Am J Hum Genet403;Algorithms *Genotype Humans *Pedigree Phenotype ProbabilityMarAutomatic genotype elimination algorithms for a single locus play a central role in making likelihood computations on human pedigree data feasible. We present a simple algorithm that is fully efficient in pedigrees without loops. This algorithm can be easily coded and has been instrumental in greatly reducing computing times for pedigree analysis. A contrived counter-example demonstrates that some superfluous genotypes cannot be excluded for inbred pedigrees.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3578274 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.3578274!~?$Lange, K. Westlake, J. Spence, M. A.1976OExtensions to pedigree analysis. III. Variance components by the scoring method485-91 Ann Hum Genet394CFemale Genotype Humans Male *Models, Biological *Pedigree PhenotypeMaylThe classic variance components for simple polygenic traits - additive, dominance, and environmental variance - have traditionally been estimated from sample covariances between first-degree relatives. If data is gathered on pedigrees, this statistical procedure wastes information. Recently Elston & Stewart suggested an alternative likelihood procedure that uses all the information in a set of pedigrees. A refinement of their method based on the scoring technique gives rapidly converging maximum likelihood estimates of the variance components and of the male and female means. Tests of statistical hypotheses about the various parameters can then be made by the likelihood ratio method. Furthermore, using classical regression analysis, the estimated parameter values allow prediction of unknown trait values from known trait values within a pedigree. These methods should apply to traits like total finger ridge count and to quantitative measurements associated with disease traits. Since the model postulates independent environmental effects and no assortative mating, its utility in human behaviour genetics seems limited.dhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=952492 F0003-4800 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.952492.~?Lansbury, P. T., Jr.2004TBack to the future: the 'old-fashioned' way to new medications for neurodegenerationS51-7Nat Med10 SupplAlzheimer Disease/therapy Animals Case-Control Studies Disease Models, Animal Humans Models, Neurological Neural Networks (Computer) Neurodegenerative Diseases/diagnosis/*therapy Parkinson Disease/therapyJulDespite the increasing prevalence of Alzheimer's disease, Parkinson's disease and less common neurodegenerative diseases-and despite the large amount of primary research that has been carried out into the causes and pathogenic features of these conditions-progress toward effective treatments has been remarkably slow. Why is this, and what can be done to accelerate it? There are a number of obstacles to effective drug discovery for neurodegeneration, but by considering these problems it is possible to identify lessons for the future.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15298008 (1078-8956 (Print) Journal Article Review15298008Harvard Center for Neurodegeneration and Repair and the Department of Neurology, Harvard Medical School, Center for Neurologic Diseases, Brigham and Women's Hospital, 65 Landsdowne St., Cambridge, Massachusetts 02139, USA. plansbury@rics.bwh.harvard.edu$~?8Lathrop, G. M. Hooper, A. B. Huntsman, J. W. Ward, R. H.1983aEvaluating pedigree data. I. The estimation of pedigree error in the presence of marker mistyping241-62Am J Hum Genet352Alleles Child Diagnostic Errors Evaluation Studies *Genetic Markers Humans Linkage (Genetics) Models, Genetic Paternity *Pedigree ProbabilityMarPedigrees used in the analysis of genetic or medical data are usually ascertained from sources subject to a variety of errors including misidentification of individuals, faults in historical documents or record linkage, nonpaternity, and unidentified adoption. Genetic markers can be used to verify putative family and pedigree data through the search for inconsistencies, or genetic exclusions, between putative parents and offspring. The probability of observing an exclusion given the occurrence of an error depends upon the gene frequencies at the loci under study and the forms of error. In addition, inconsistencies can arise from laboratory errors in marker determination. Together, these problems make the proper statistical analysis of such data desirable. Here we give a model that specifies the combined effects of various kinds of pedigree error along with genetic marker error. This model allows the maximum-likelihood estimation of the rates of various forms of pedigree error and laboratory error from genetic marker data collected on putative families. The method is illustrated by applying it to data obtained from a South Pacific island population, Tokelau. From the observed distribution of genetic marker inconsistencies between the parents and offspring of putative families, derived from the extensive genealogy of this population, we are able to estimate that the error of a paternal link is 4%, the error of a maternal link is zero, and the overall system typing error is 1%.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6573130 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.65731306~?0Lathrop, G. M. Lalouel, J. M. Julier, C. Ott, J.19844Strategies for multilocus linkage analysis in humans3443-6Proc Natl Acad Sci U S A8111*Chromosome Mapping *Chromosomes, Human Genes, Dominant Genes, Recessive Humans *Linkage (Genetics) Pedigree Polymorphism, GeneticJunhThe increasing number of DNA polymorphisms characterized in humans will soon allow the construction of fine genetic maps of human chromosomes. This advance calls for a reexamination of current methodologies for linkage analysis by the family method. We have investigated the relative efficiency of two-point and three-point linkage tests for the detection of linkage and the estimation of recombination in a variety of situations. This led us to develop the computer program LINKAGE to perform multilocus linkage analysis. The investigation also enables us to propose a method of location scores for the efficient detection of linkage between a disease locus, or a new genetic marker, and a linkage group previously established from a reference panel of families. The method is illustrated by an application to simulated pedigree data in a situation akin to Duchenne muscular dystrophy. These results show that considerable economy and efficiency can be brought to the mapping endeavor by resorting to appropriate strategies of detecting linkage and by constructing the human genetic map on a common reference panel of families.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6587361 !0027-8424 (Print) Journal Article6587361~?0Lathrop, G. M. Lalouel, J. M. Julier, C. Ott, J.1985[Multilocus linkage analysis in humans: detection of linkage and estimation of recombination482-98Am J Hum Genet373*Chromosome Mapping Genes Genetic Markers Humans *Linkage (Genetics) *Models, Genetic Pedigree Probability *Recombination, GeneticMayMultilocus linkage analysis is investigated from the viewpoint of the efficiency of recombination estimates under different strategies for detecting linkage and determining gene order within a linkage group. We consider the appropriateness of assuming no interference with data available in human genetic studies. Examples are given to show the significance of multilocus analysis in humans. A computer program package, LINKAGE, for multilocus linkage analysis is described.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3859205 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't3859205? Lawley, D.N.1940EThe estimation of factor loadings by the method of maximum likelihood64-82Proc. R. Soc. Edinb. Sec. A60}? Lawley, D.N.1941+Further investigations in factor estimation176-185 Proc. R. Soc. Edinb. Sec. A61F? Lawley, D.N.1943DThe application of the maximum likelihood method to factor analysis.Br. J. Psychol. 33172-175? Lawley, D.N.19677Some new results in maximum likelihood factor analysis.256-264Proc. R. Soc. Edinb. Sec. A67~? Leal, S. M.2003fGenetic maps of microsatellite and single-nucleotide polymorphism markers: are the distances accurate?243-52Genet Epidemiol244Adult Child *Chromosome Mapping/standards Computer Simulation Confidence Intervals Humans Meiosis Microsatellite Repeats/*genetics Polymorphism, Single Nucleotide/*geneticsMayGenetic maps play an important role in gene mapping. Inaccurate genetic maps can hinder gene mapping by biasing lod scores and reducing the power to map a trait to a particular region. Although sequence-based physical maps can provide a unique order for markers, they do not provide information on genetic map distances. By simulation studies, I investigated how many meioses are necessary to accurately estimate genetic map distances for maps constructed from microsatellite and single-nucleotide polymorphism (SNP) markers for various intermarker distances and marker heterozygosity. To evaluate the accuracy of the generated genetic maps, the length of the 95% confidence interval for intermarker genetic distances was examined. In addition, the power to separate two adjacent markers by a nonzero map distance was investigated. The number of meioses necessary to accurately estimate map distances depends greatly not only on intermarker distances but also on marker heterozygosity. For example, for a genetic map with intermarker distances of 0.5 cM generated with 1,000 meioses, when marker heterozygosity was high (0.90), for 96% of the markers there was a nonzero map distance between adjacent markers. However, when marker heterozygosity was low (0.32), only 48% of the markers mapped to a unique position. For identical numbers of meioses and intermarker distances, genetic maps constructed from microsatellite markers will be more precise than maps assembled from SNP markers, due to the higher levels of heterozygosity for microsatellite markers.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12687641 Y0741-0395 (Print) Evaluation Studies Journal Article Research Support, U.S. Gov't, P.H.S.12687641tDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA. sleal@bcm.tmc.edu )~? Leal, S. M.2005]Detection of genotyping errors and pseudo-SNPs via deviations from Hardy-Weinberg equilibrium204-14Genet Epidemiol293Gene Frequency *Genotype Humans Linkage Disequilibrium/*genetics Models, Genetic Models, Statistical Polymorphism, Single Nucleotide/*genetics Sample SizeNovGenotype error can greatly reduce the power of a genetic study. For family data, genotype error can be assessed by examining marker data for non-Mendelian inconsistencies, closely linked markers for double recombination events, and consistency of duplicate genotypes. For case-control data, duplicate samples are genotyped, and controls are tested for deviations from Hardy-Weinberg equilibrium (HWE). Duplicate samples can provide accurate estimates of genotyping error rates, unless systematic genotyping errors have occurred. Although genotyping errors can cause deviations from HWE, these deviations are usually small, and the power to detect them is low except for high rates of genotyping error and/or large sample sizes. An additional problem is that even when deviations from HWE are detected for marker loci, without additional experimentation it is not possible to unequivocally implicate genotyping error as the cause. The power and sample sizes necessary to detect deviations from HWE for single-nucleotide polymorphism (SNP) data are examined for a variety of genotyping error and pseudo-SNP models. For the majority of genotyping models examined, the power is poor to detect deviations from HWE. For example, for 1,000 controls, if an allele with a frequency of 0.1 fails to amplify for 28% of the heterozygous genotypes producing a sample error rate of 0.05, the power is 0.51 to detect a deviation from HWE at an alpha level of 0.05. On the other hand, the detection of deviations from HWE for pseudo-SNPs (paralogous and ectopic sequence variants) for the majority of models examined produces a power of >0.8 for sample sizes as small as 50 individuals.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16080207 k0741-0395 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S.16080207tDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA. sleal@bcm.tmc.edu~?Lewis, A. Reik, W.2006How imprinting centres work81-9Cytogenet Genome Res1131-4Animals Chromatin/genetics Female *Genomic Imprinting Male Mammals/genetics *Models, Genetic Multigene Family RNA/genetics X ChromosomerImprinted genes tend to be clustered in the genome. Most of these clusters have been found to be under the control of discrete DNA elements called imprinting centres (ICs) which are normally differentially methylated in the germline. ICs can regulate imprinted expression and epigenetic marks at many genes in the region, even those which lie several megabases away. Some of the molecular and cellular mechanisms by which ICs control other genes and regulatory regions in the cluster are becoming clear. One involves the insulation of genes on one side of the IC from enhancers on the other, mediated by the insulator protein CTCF and higher-order chromatin interactions. Another mechanism may involve non-coding RNAs that originate from the IC, targeting histone modifications to the surrounding genes. Given that several imprinting clusters contain CTCF dependent insulators and/or non-coding RNAs, it is likely that one or both of these two mechanisms regulate imprinting at many loci. Both mechanisms involve a variety of epigenetic marks including DNA methylation and histone modifications but the hierarchy of and interactions between these modifications are not yet understood. The challenge now is to establish a chain of developmental events beginning with differential methylation of an IC in the germline and ending with imprinting of many genes, often in a lineage dependent manner.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16575166 N1424-859X (Electronic) Journal Article Research Support, Non-U.S. Gov't Review16575166wLaboratory of Developmental Genetics and Imprinting, The Babraham Institute, Cambridge, UK. annabelle.lewis@bbsrc.ac.uk}?Lewontin, R. Kojima, K19602The evolutionary dynamics of complex polymorphisms458-472 Evolution 14x~?Lewontin, R. C.1964UThe Interaction of Selection and Linkage. I. General Considerations; Heterotic Models49-67Genetics491Janfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17248194 !0016-6731 (Print) Journal Article17248194?Department of Biology, University of Rochester, Rochester, N.Y.w~? Li, C. C.19697Population subdivision with respect to multiple alleles23-9 Ann Hum Genet331`*Alleles Gene Frequency *Genetics, Population Genotype Germ Cells Heterozygote Humans InbreedingJulehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=5821316 !0003-4800 (Print) Journal Article5821316 ~?Li, Z. K. Yu, S. B. Lafitte, H. R. Huang, N. Courtois, B. Hittalmani, S. Vijayakumar, C. H. Liu, G. F. Wang, G. C. Shashidhar, H. E. Zhuang, J. Y. Zheng, K. L. Singh, V. P. Sidhu, J. S. Srivantaneeyakul, S. Khush, G. S.2003HQTL x environment interactions in rice. I. heading date and plant height141-53Theor Appl Genet1081*Chromosome Mapping DNA, Plant/genetics *Environment *Epistasis, Genetic Genetic Markers Genotype Linkage (Genetics) Oryza sativa/*genetics/growth & development *Phenotype *Quantitative Trait, HeritableDecOne hundred twenty six doubled-haploid (DH) rice lines were evaluated in nine diverse Asian environments to reveal the genetic basis of genotype x environment interactions (GEI) for plant height (PH) and heading date (HD). A subset of lines was also evaluated in four water-limited environments, where the environmental basis of G x E could be more precisely defined. Responses to the environments were resolved into individual QTL x environment interactions using replicated phenotyping and the mixed linear-model approach. A total of 37 main-effect QTLs and 29 epistatic QTLs were identified. On average, these QTLs were detectable in 56% of the environments. When detected in multiple environments, the main effects of most QTLs were consistent in direction but varied considerably in magnitude across environments. Some QTLs had opposite effects in different environments, particularly in water-limited environments, indicating that they responded to the environments differently. Inconsistent QTL detection across environments was due primarily to non- or weak-expression of the QTL, and in part to significant QTL x environment interaction effects in the opposite direction to QTL main effects, and to pronounced epistasis. QTL x environment interactions were trait- and gene-specific. The greater GEI for HD than for PH in rice were reflected by more environment-specific QTLs, greater frequency and magnitude of QTL x environment interaction effects, and more pronounced epistasis for HD than for PH. Our results demonstrated that QTL x environment interaction is an important property of many QTLs, even for highly heritable traits such as height and maturity. Information about QTL x environment interaction is essential if marker-assisted selection is to be applied to the manipulation of quantitative traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12961067 T0040-5752 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't12961067Plant Breeding, Genetics, and Biochemistry Division, International Rice Research Institute, DAPO Box 7777, Metro Manila, The Philippines. z.li@cgiar.orgh~?%Lin, S. Chakravarti, A. Cutler, D. J.2004iExhaustive allelic transmission disequilibrium tests as a new approach to genome-wide association studies1181-8 Nat Genet3611/Algorithms Alleles Chromosome Mapping Computer Simulation Genetic Diseases, Inborn/*genetics *Genetic Markers Genetic Predisposition to Disease *Genome, Human Haplotypes Hirschsprung Disease/genetics Humans Models, Genetic Models, Statistical *Polymorphism, Single Nucleotide Sensitivity and SpecificityNovGenome-wide disease-association mapping has been heralded as the study design of the next generation, but the lack of analytical methods to use genotype data fully is a large stumbling block. Here we describe an algorithm and statistical method that efficiently and exhaustively exploits haplotype information by subjecting alleles (a marker or contiguous sets of markers) from sliding windows of all sizes to transmission disequilibrium tests. By applying our method to simulated data and to Hirschsprung disease, we show that it can detect both common and rare disease variants of small effect. These results show that the theoretical benefits of genome-wide association studies are at last realizable.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15502828 z1061-4036 (Print) Evaluation Studies Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15502828McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Broadway Research Building, Suite 475, 733 N. Broadway, Baltimore, Maryland 21205, USA.w~?Lin, Z. Altman, R. B.2004FFinding haplotype tagging SNPs by use of principal components analysis850-61Am J Hum Genet755Chromosome Mapping/methods Evaluation Studies Genomics/*methods Genotype Haplotypes/*genetics Humans Polymorphism, Single Nucleotide/*genetics Principal Component Analysis/*methodsNovThe immense volume and rapid growth of human genomic data, especially single nucleotide polymorphisms (SNPs), present special challenges for both biomedical researchers and automatic algorithms. One such challenge is to select an optimal subset of SNPs, commonly referred as "haplotype tagging SNPs" (htSNPs), to capture most of the haplotype diversity of each haplotype block or gene-specific region. This information-reduction process facilitates cost-effective genotyping and, subsequently, genotype-phenotype association studies. It also has implications for assessing the risk of identifying research subjects on the basis of SNP information deposited in public domain databases. We have investigated methods for selecting htSNPs by use of principal components analysis (PCA). These methods first identify eigenSNPs and then map them to actual SNPs. We evaluated two mapping strategies, greedy discard and varimax rotation, by assessing the ability of the selected htSNPs to reconstruct genotypes of non-htSNPs. We also compared these methods with two other htSNP finders, one of which is PCA based. We applied these methods to three experimental data sets and found that the PCA-based methods tend to select the smallest set of htSNPs to achieve a 90% reconstruction precision.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15389393 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.15389393^Department of Genetics, School of Medicine, Stanford University, Stanford, CA 94305-5120, USA. P~?DLindsay, R. S. Kobes, S. Knowler, W. C. Bennett, P. H. Hanson, R. L.2001}Genome-wide linkage analysis assessing parent-of-origin effects in the inheritance of type 2 diabetes and BMI in Pima Indians2850-7Diabetes5012Aging Alleles *Body Mass Index Chromosomes, Human, Pair 10 Chromosomes, Human, Pair 14 Chromosomes, Human, Pair 5 Chromosomes, Human, Pair 6 Diabetes Mellitus, Type 2/*genetics Female *Genomic Imprinting Humans *Indians, North American *Linkage (Genetics) Lod Score Male Nuclear FamilyDecWe examined the hypothesis that imprinted genes may affect the propensity to type 2 diabetes and obesity in Pima Indians. Multipoint variance component methods were used to assess linkage of BMI (kg/m(2)) and age-adjusted diabetes to loci derived from either father (LOD(FA)) or mother (LOD(MO)) in a genome-wide scan. Tentative evidence of loci where imprinted genes might be acting was found for diabetes with maternally derived alleles on chromosomes 5 (LOD(MO) = 1.5) and 14 (LOD(MO) = 1.6). Evidence of linkage of BMI to maternally derived alleles was found on chromosome 5 (LOD(MO) = 1.7) and to paternally derived alleles on chromosome 10p (LOD(FA) = 1.7). Additional analyses of sibling pairs who were affected by diabetes and younger than 25 years of age showed an increase of sharing of maternally derived alleles on chromosome 6 (LOD(MO) = 3.0). We also examined sites of a priori interest where action of imprinted genes has been proposed in diabetes or obesity. We found no evidence of parent-specific linkage (of either diabetes or BMI) on chromosome 11p, a region that contains several imprinted genes, but observed weak evidence of linkage of diabetes to paternally derived alleles (LOD(FA) = 0.9) in the region of chromosome 6q, believed to contain an exclusively paternally expressed gene or genes that cause transient neonatal diabetes mellitus. In conclusion, we determined regions of interest on chromosomes 5, 6, and 10 where imprinted genes might be affecting the risk of type 2 diabetes or obesity in Pima Indians.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11723070 !0012-1797 (Print) Journal Article11723070National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona 85014, USA. rlindsay@mail.nih.gov~?5Lindsay, R. S. Kobes, S. Knowler, W. C. Hanson, R. L.2002bGenome-wide linkage analysis assessing parent-of-origin effects in the inheritance of birth weight503-9 Hum Genet1105.Birth Weight/*genetics Chromosome Mapping/*methods Chromosomes, Human, Pair 21/genetics Chromosomes, Human, Pair 3/genetics Diabetes Mellitus, Type 2/genetics Female Gene Frequency Genome, Human Gestational Age Humans Indians, North American/*genetics Lod Score Male Multifactorial Inheritance/geneticsMayiFamily studies suggest that genetic variation may influence birth weight. We have assessed linkage of birth weight in a genome-wide scan in 269 Pima Indian siblings (334 sibling pairs, 92 families). As imprinting (expression of only a single copy of a gene depending on parent-of-origin), is commonly found in genes that affect fetal growth, we used a recently described modification of standard multipoint variance-component methods of linkage analysis of quantitative traits. This technique allows for comparison of linkage models that incorporate imprinting effects (in which the strength of linkage is expressed as LOD(IMP)) and models where parent-of-origin effects are not included (LOD(EQ)). Where significant evidence of linkage was present, separate contributions of alleles derived from father (LOD(FA)) or mother (LOD(MO)) to the imprinting model were estimated. Significant evidence of linkage was found on chromosome 11 (at map position 88 cM, LOD(IMP)=3.4) with evidence for imprinting (imprinting model superior, P<0.001). In this region, birth weight was linked predominantly to paternally derived alleles (LOD(FA)=4.1, LOD(MO)=0.0). An imprinted gene on chromosome 11 may influence birth weight in the Pima population. This chromosome contains one of the two major known clusters of imprinted genes in the human genome, lending biological plausibility to our findings.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12073022 !0340-6717 (Print) Journal Article12073022National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 1550 East Indian School Road, Phoenix, AZ 85014, USA. rlindsay@mail.nih.gov~?Litt, M. Luty, J. A.1989A hypervariable microsatellite revealed by in vitro amplification of a dinucleotide repeat within the cardiac muscle actin gene397-401Am J Hum Genet443Actins/*genetics DNA, Satellite/*genetics DNA-Directed DNA Polymerase Female Genetic Markers Humans Linkage (Genetics) Male Myocardium/analysis Pedigree Polymorphism, Restriction Fragment Length *Repetitive Sequences, Nucleic AcidMarThe human genome contains approximately 50,000 copies of an interspersed repeat with the sequence (dT-dG)n, where n = approximately 10-60. In humans, (TG)n repeats have been found in several sequenced regions. Since minisatellite regions with larger repeat elements often display extensive length polymorphisms, we suspected that (TG)n repeats ("microsatellites") might also be polymorphic. Using the polymerase chain reaction to amplify a (TG)n microsatellite in the human cardiac actin gene, we detected 12 different allelic fragments in 37 unrelated individuals, 32 of whom were heterozygous. Codominant Mendelian inheritance of fragments was observed in three families with a total of 24 children. Because of the widespread distribution of (TG)n microsatellites, polymorphisms of this type may be generally abundant and present in regions where minisatellites are rare, making such microsatellite loci very useful for linkage studies in humans.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2563634 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.2563634NDepartment of Biochemistry, Oregon Health Sciences University, Portland 97201.q?Little, R.J.A Rubin, D.B1987'Statistical Analysis with Missing Data. New York, NY.WileyvO?Little, R.J.A Rubin, D.B2002&Statistical Analysis with Missing Data New York, NY.Wiley2nd?Loehlin, J.C. Vandenberg, S.G.1968aGenetic and environmental components in the covariation of cognitive abilities: an additive model261-278%Progress in Human Behaviour Genetics.S.G. Vandenberg Baltimore, MD John Hopkins ~?HLohmueller, K. E. Pearce, C. L. Pike, M. Lander, E. S. Hirschhorn, J. N.2003{Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease177-82 Nat Genet332Alleles Data Interpretation, Statistical False Positive Reactions Follow-Up Studies *Genetic Predisposition to Disease Genetics, Population Genome, Human Humans Linkage Disequilibrium Polymorphism, Single Nucleotide Publication Bias Reproducibility of Results *Variation (Genetics)FebAssociation studies offer a potentially powerful approach to identify genetic variants that influence susceptibility to common disease, but are plagued by the impression that they are not consistently reproducible. In principle, the inconsistency may be due to false positive studies, false negative studies or true variability in association among different populations. The critical question is whether false positives overwhelmingly explain the inconsistency. We analyzed 301 published studies covering 25 different reported associations. There was a large excess of studies replicating the first positive reports, inconsistent with the hypothesis of no true positive associations (P < 10(-14)). This excess of replications could not be reasonably explained by publication bias and was concentrated among 11 of the 25 associations. For 8 of these 11 associations, pooled analysis of follow-up studies yielded statistically significant replication of the first report, with modest estimated genetic effects. Thus, a sizable fraction (but under half) of reported associations have strong evidence of replication; for these, false negative, underpowered studies probably contribute to inconsistent replication. We conclude that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12524541 u1061-4036 (Print) Journal Article Meta-Analysis Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.12524541oWhitehead/Massachuetts Institute of Technology Center for Genome Research, Cambridge, Massachusetts 02139, USA. ~?Long, A. D. Langley, C. H.1999tThe power of association studies to detect the contribution of candidate genetic loci to variation in complex traits720-31 Genome Res98Computational Biology/methods/statistics & numerical data Genetic Markers/genetics Genotype Humans Linkage Disequilibrium/genetics Nucleotides/genetics Phenotype Polymorphism, Genetic/genetics *Quantitative Trait, Heritable Variation (Genetics)/*geneticsAugThe statistical power of five association study test statistics (two haplotype-based tests, two marker-based tests, and the Transmission Disequilibrium Test-Q5) to detect single nucleotide polymorphism (SNP)/phenotype associations in a linkage-disequilibrium-based candidate gene scan employing a number of SNPs is examined. Power is estimated as a function of realistic parameters expected to affect the likelihood of detecting a significant association: the number of SNPs examined, the scaled recombination size of the region examined, the proportion of variance in the trait attributable to a hidden causative polymorphism within the region, and the number of individuals or families examined. For the different combinations of parameter values, power is estimated from a large number of realizations of a simulated coalescent describing a single random mating population with mutation, random genetic drift, and recombination. This explicit population genetics model results in a distribution of DNA marker heterozygosities and linkage disequilibria that are likely to resemble those expected in actual population samples. The study concludes that (1) marker-based permutation tests are more powerful than simple haplotype-based tests, (2) there is sufficient power to detect the presence of causative polymorphisms of small effect if on the order of 500 individuals are sampled, (3) greater power is achieved by increasing the sample size than by increasing the number of polymorphisms, (4) association studies are generally more powerful than transmission disequilibrium-based tests, and (5) for the range of parameters considered association studies have a low repeatability unless sample sizes are on the order of 500 individuals. Estimates of 4Nc for a number of gene regions and human populations will be of use in determining the density of SNPs that are likely to be required for successful association studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10447507 k1088-9051 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.10447507Department of Ecology and Evolutionary Biology, University of California at Irvine, Irvine, California 92697-2525, USA. tdlong@uci.edu ~?'Long, J. C. Williams, R. C. Urbanek, M.1995CAn E-M algorithm and testing strategy for multiple-locus haplotypes799-810Am J Hum Genet563*Algorithms Alleles Chromosome Mapping Data Interpretation, Statistical Genotype *Haplotypes Humans Linkage Disequilibrium *Models, Genetic PhenotypeMarVThis paper gives an expectation maximization (EM) algorithm to obtain allele frequencies, haplotype frequencies, and gametic disequilibrium coefficients for multiple-locus systems. It permits high polymorphism and null alleles at all loci. This approach effectively deals with the primary estimation problems associated with such systems; that is, there is not a one-to-one correspondence between phenotypic and genotypic categories, and sample sizes tend to be much smaller than the number of phenotypic categories. The EM method provides maximum-likelihood estimates and therefore allows hypothesis tests using likelihood ratio statistics that have chi 2 distributions with large sample sizes. We also suggest a data resampling approach to estimate test statistic sampling distributions. The resampling approach is more computer intensive, but it is applicable to all sample sizes. A strategy to test hypotheses about aggregate groups of gametic disequilibrium coefficients is recommended. This strategy minimizes the number of necessary hypothesis tests while at the same time describing the structure of disequilibrium. These methods are applied to three unlinked dinucleotide repeat loci in Navajo Indians and to three linked HLA loci in Gila River (Pima) Indians. The likelihood functions of both data sets are shown to be maximized by the EM estimates, and the testing strategy provides a useful description of the structure of gametic disequilibrium. Following these applications, a number of simulation experiments are performed to test how well the likelihood-ratio statistic distributions are approximated by chi 2 distributions. In most circumstances the chi 2 grossly underestimated the probability of type I errors. However, at times they also overestimated the type 1 error probability. Accordingly, we recommended hypothesis tests that use the resampling method.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7887436 J0002-9297 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S.7887436<Laboratory of Neurogenetics, NIAAA/NIH, Rockville, MD 20852.r|7 Louis, T. A.1982CFinding the Observed Information Matrix When Using the Em Algorithm226-233@Journal of the Royal Statistical Society Series B-Methodological442://A1982PF14100009/Pf141 Times Cited:484 Cited References Count:14 0035-9246ISI:A1982PF141000094Louis, Ta Harvard Univ,Sch Publ Hlth,Boston,Ma 02115English@~?&Lubke, G. H. Dolan, C. V. Neale, M. C.2004vImplications of absence of measurement invariance for detecting sex limitation and genotype by environment interaction292-8Twin Res73Algorithms Environment Female Genotype Humans Male *Models, Theoretical Multivariate Analysis Regression Analysis Sex Factors Twins/genetics/*physiology Twins, Dizygotic/genetics/physiology Twins, Monozygotic/genetics/physiologyJun Using univariate sum scores in genetic studies of twin data is common practice. This practice precludes an investigation of the measurement model relating the individual items to an underlying factor. Absence of measurement invariance across a grouping variable such as gender or environmental exposure refers to group differences with respect to the measurement model. It is shown that a decomposition of a sum score into genetic and environmental variance components leads to path coefficients of the additive genetic factor that are biased differentially across groups if individual items are non-invariant. The arising group differences in path coefficients are identical to what is known as "scalar sex limitation" when gender is the grouping variable, or as "gene by environment interaction" when environmental exposure is the grouping variable. In both cases the interpretation would be in terms of a group-specific effect size of the genetic factor. This interpretation may be incorrect if individual items are non-invariant.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15193174 y1369-0523 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15193174Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond 23219-1534, USA. glubke@vcu.edup~?'Luciano, M. Wright, M. J. Martin, N. G.2004aExploring the etiology of the association between birthweight and IQ in an adolescent twin sample62-71Twin Res71qAdolescent *Birth Weight Female Humans Intelligence/*genetics Intelligence Tests/statistics & numerical data MaleFeb The negative effects of very low birthweight on intellectual development have been well documented, and more recently this effect has been shown to generalize to birthweights within the normal range. In this study we investigate the etiology of this relationship by using a classical twin design to disentangle the contributions of genes and environment. A previous Dutch study (Boomsma et al., 2001) examining these effects indicated that genes were important in mediating the association of birthweight to full IQ measured at ages 7 and 10, but not at ages 5 and 12. Here the association between birthweight and IQ at age 16 is considered (N = 523 twin pairs). Using variance components modeling we found that the genetic variance in birthweight (4%) completely overlapped with that in verbal IQ but not performance or full IQ. Results further showed the importance of shared environmental effects on birthweight (~ 60%) but not on IQ (with genes explaining up to 72% of IQ variance). Models incorporating a direction of causation parameter between birthweight and IQ provided adequate fit to the data in either causal direction for performance and full IQ, but the model with verbal IQ causing birthweight was preferred to one in which birthweight influenced verbal IQ. As the measurement of birthweight precedes the measurement of twins' IQ at age 16, the influence of verbal IQ might be better considered as a proxy for parents' IQ or education, and it is possible that brighter mothers provide better prenatal environments for their children.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15053855 _1369-0523 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Twin Study15053855SQueensland Institute of Medical Research, Brisbane, Australia. michelLu@qimr.edu.auw?Lynch, M. Walsh, B.1998-Genetics and Analysis of Quantitative Traits.Sunderland, MA.Sinauer~?4Macgregor, S. Knott, S. A. White, I. Visscher, P. M.2005^Quantitative trait locus analysis of longitudinal quantitative trait data in complex pedigrees1365-76Genetics1713Chromosome Mapping/statistics & numerical data Computer Simulation Humans *Models, Genetic *Pedigree *Quantitative Trait Loci *Quantitative Trait, HeritableNov3There is currently considerable interest in genetic analysis of quantitative traits such as blood pressure and body mass index. Despite the fact that these traits change throughout life they are commonly analyzed only at a single time point. The genetic basis of such traits can be better understood by collecting and effectively analyzing longitudinal data. Analyses of these data are complicated by the need to incorporate information from complex pedigree structures and genetic markers. We propose conducting longitudinal quantitative trait locus (QTL) analyses on such data sets by using a flexible random regression estimation technique. The relationship between genetic effects at different ages is efficiently modeled using covariance functions (CFs). Using simulated data we show that the change in genetic effects over time can be well characterized using CFs and that including parameters to model the change in effect with age can provide substantial increases in power to detect QTL compared with repeated measure or univariate techniques. The asymptotic distributions of the methods used are investigated and methods for overcoming the practical difficulties in fitting CFs are discussed. The CF-based techniques should allow efficient multivariate analyses of many data sets in human and natural population genetics.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16020786 B0016-6731 (Print) Journal Article Research Support, Non-U.S. Gov't16020786rInstitute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3JT, United Kingdom. macgregors@cf.ac.uk~?Maeda, N. Hayashizaki, Y.2006%Genome-wide survey of imprinted genes144-52Cytogenet Genome Res1131-4Animals DNA/genetics DNA Methylation Dinucleoside Phosphates/analysis Female *Genome *Genomic Imprinting Models, Genetic Ovum/physiology Polymorphism, Genetic Restriction MappingThe developmental failure of mammalian parthenogenote has been a mystery for a long time and posed a question as to why bi-parental reproduction is necessary for development to term. In the 1980s, it was proven that this failure was not due to the genetic information itself, but to epigenetic modification of genomic DNA. In the following decade, several studies successfully identified imprinted genes which were differentially expressed in a parent-of-origin-specific manner, and it was shown that the differential expression depended on the pattern of DNA methylation. These facts prompted development of genome-wide systematic screening methods based on DNA methylation and differential gene expression to identify imprinted genes. Recently computational approaches and microarray technology have been introduced to identify imprinted genes/loci, contributing to the expansion of our knowledge. However, it has been shown that the gene silencing derived from genomic imprinting is accomplished by several mechanisms in addition to direct DNA methylation, indicating that novel approaches are further required for comprehensive understanding of genomic imprinting. To unveil the mechanism of developmental failure in mammalian parthenogenote, systematic screenings for imprinted genes/loci have been developed. In this review, we describe genomic imprinting focusing on the history of genome-wide screening.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16575174 -1424-859X (Electronic) Journal Article Review16575174SGenome Science Laboratory, Discovery and Research Institute, RIKEN, Saitama, Japan. F~?FManiatis, N. Collins, A. Gibson, J. Zhang, W. Tapper, W. Morton, N. E.2004,Positional cloning by linkage disequilibrium846-55Am J Hum Genet745 Alleles *Chromosome Mapping Chromosomes, Human, Pair 5/genetics Chromosomes, Human, Pair 6/genetics *Cloning, Molecular Gene Frequency/genetics Haplotypes/genetics Humans Likelihood Functions Linkage Disequilibrium/*genetics Polymorphism, Single Nucleotide/geneticsMayXRecently, metric linkage disequilibrium (LD) maps that assign an LD unit (LDU) location for each marker have been developed (Maniatis et al. 2002). Here we present a multiple pairwise method for positional cloning by LD within a composite likelihood framework and investigate the operating characteristics of maps in physical units (kb) and LDU for two bodies of data (Daly et al. 2001; Jeffreys et al. 2001) on which current ideas of blocks are based. False-negative indications of a disease locus (type II error) were examined by selecting one single-nucleotide polymorphism (SNP) at a time as causal and taking its allelic count (0, 1, or 2, for the three genotypes) as a pseudophenotype, Y. By use of regression and correlation, association between every pseudophenotype and the allelic count of each SNP locus (X) was based on an adaptation of the Malecot model, which includes a parameter for location of the putative gene. By expressing locations in kb or LDU, greater power for localization was observed when the LDU map was fitted. The efficiency of the kb map, relative to the LDU map, to describe LD varied from a maximum of 0.87 to a minimum of 0.36, with a mean of 0.62. False-positive indications of a disease locus (type I error) were examined by simulating an unlinked causal SNP and the allele count was used as a pseudophenotype. The type I error was in good agreement with Wald's likelihood theorem for both metrics and all models that were tested. Unlike tests that select only the most significant marker, haplotype, or haploset, these methods are robust to large numbers of markers in a candidate region. Contrary to predictions from tagging SNPs that retain haplotype diversity, the sample with smaller size but greater SNP density gave less error. The locations of causal SNPs were estimated with the same precision in blocks and steps, suggesting that block definition may be less useful than anticipated for mapping a causal SNP. These results provide a guide to efficient positional cloning by SNPs and a benchmark against which the power of positional cloning by haplotype-based alternatives may be measured.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15048619 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't15048619pHuman Genetics Division, University of Southampton, Southampton SO16 6YD, United Kingdom. N.Maniatis@soton.ac.uk w~?JManiatis, N. Morton, N. E. Gibson, J. Xu, C. F. Hosking, L. K. Collins, A.2005fThe optimal measure of linkage disequilibrium reduces error in association mapping of affection status145-53 Hum Mol Genet141Chromosomes, Human, Pair 22/genetics Cytochrome P-450 CYP2D6/genetics Genetic Predisposition to Disease/*genetics Humans Linkage Disequilibrium/*genetics Metabolic Detoxication, Drug/genetics *Models, Genetic Polymorphism, Single Nucleotide/geneticsJan 1QWe have developed a simple yet powerful approach for disease gene association mapping by linkage disequilibrium (LD). This method is unique because it applies a model with evolutionary theory that incorporates a parameter for the location of the causal polymorphism. The method exploits LD maps, which assign a location in LD units (LDU) for each marker. This approach is based on single marker tests within a composite likelihood framework, which avoids the heavy Bonferroni correction through multiple testing. As a proof of principle, we tested an 890 kb region flanking the CYP2D6 gene associated with poor drug-metabolizing activity in order to refine the localization of a causal mutation. Previous LD mapping studies using single markers and haplotypes have identified a 390 kb significant region associated with the poor drug-metabolizing phenotype on chromosome 22. None of the 27 Single nucleotide polymorphisms was within the gene. Using a metric LDU map, the commonest functional polymorphism within the gene was located at 14.9 kb from its true location, surrounded within a 95% confidence interval of 172 kb. The kb map had a relative efficiency of 33% compared with the LDU map. Our findings indicate that the support interval and location error are smaller than any published results. Despite the low resolution and the strong LD in the region, our results provide evidence of the substantial utility of LDU maps for disease gene association mapping. These tests are robust to large numbers of markers and are applicable to haplotypes, diplotypes, whole-genome association or candidate region studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15548543 B0964-6906 (Print) Journal Article Research Support, Non-U.S. Gov't15548543yHuman Genetics Division, University of Southampton, Southampton General Hospital, Southampton, UK. n.maniatis@soton.ac.uk ~~?Maraganore, D. M. de Andrade, M. Lesnick, T. G. Strain, K. J. Farrer, M. J. Rocca, W. A. Pant, P. V. Frazer, K. A. Cox, D. R. Ballinger, D. G.2005CHigh-resolution whole-genome association study of Parkinson disease685-93Am J Hum Genet775Adult Aged Aged, 80 and over Chromosome Mapping/*methods Female Gene Frequency/genetics Genetic Markers/genetics Genetic Predisposition to Disease/genetics Genome, Human Genotype Haplotypes Humans Linkage (Genetics)/genetics Linkage Disequilibrium/genetics Male Membrane Proteins/*genetics Middle Aged Nerve Tissue Proteins/*genetics Parkinson Disease/*genetics Polymorphism, Single Nucleotide *Variation (Genetics)NovWe performed a two-tiered, whole-genome association study of Parkinson disease (PD). For tier 1, we individually genotyped 198,345 uniformly spaced and informative single-nucleotide polymorphisms (SNPs) in 443 sibling pairs discordant for PD. For tier 2a, we individually genotyped 1,793 PD-associated SNPs (P<.01 in tier 1) and 300 genomic control SNPs in 332 matched case-unrelated control pairs. We identified 11 SNPs that were associated with PD (P<.01) in both tier 1 and tier 2 samples and had the same direction of effect. For these SNPs, we combined data from the case-unaffected sibling pair (tier 1) and case-unrelated control pair (tier 2) samples and employed a liberalization of the sibling transmission/disequilibrium test to calculate odds ratios, 95% confidence intervals, and P values. A SNP within the semaphorin 5A gene (SEMA5A) had the lowest combined P value (P=7.62 x 10(-6)). The protein encoded by this gene plays an important role in neurogenesis and in neuronal apoptosis, which is consistent with existing hypotheses regarding PD pathogenesis. A second SNP tagged the PARK11 late-onset PD susceptibility locus (P=1.70 x 10(-5)). In tier 2b, we also selected for genotyping additional SNPs that were borderline significant (P<.05) in tier 1 but that tested a priori biological and genetic hypotheses regarding susceptibility to PD (n=941 SNPs). In analysis of the combined tier 1 and tier 2b data, the two SNPs with the lowest P values (P=9.07 x 10(-6); P=2.96 x 10(-5)) tagged the PARK10 late-onset PD susceptibility locus. Independent replication across populations will clarify the role of the genomic loci tagged by these SNPs in conferring PD susceptibility.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16252231 g0002-9297 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16252231MDepartment of Neurology, Mayo Clinic College of Medicine, Rochester, MN, USA.>~?7Marchini, J. Cardon, L. R. Phillips, M. S. Donnelly, P.2004NThe effects of human population structure on large genetic association studies512-7 Nat Genet365*Genetic Markers *Genetic Predisposition to Disease *Genetics, Population Humans Linkage Disequilibrium Models, Genetic Polymorphism, Single Nucleotide/*genetics Quantitative Trait, Heritable Variation (Genetics)MaylLarge-scale association studies hold substantial promise for unraveling the genetic basis of common human diseases. A well-known problem with such studies is the presence of undetected population structure, which can lead to both false positive results and failures to detect genuine associations. Here we examine approximately 15,000 genome-wide single-nucleotide polymorphisms typed in three population groups to assess the consequences of population structure on the coming generation of association studies. The consequences of population structure on association outcomes increase markedly with sample size. For the size of study needed to detect typical genetic effects in common diseases, even the modest levels of population structure within population groups cannot safely be ignored. We also examine one method for correcting for population structure (Genomic Control). Although it often performs well, it may not correct for structure if too few loci are used and may overcorrect in other settings, leading to substantial loss of power. The results of our analysis can guide the design of large-scale association studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15052271 g1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15052271WDepartment of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.~?&Markianos, K. Daly, M. J. Kruglyak, L.2001LEfficient multipoint linkage analysis through reduction of inheritance space963-77Am J Hum Genet6840*Algorithms Alleles Chromosome Mapping/*methods/statistics & numerical data Computer Simulation Computers Female Genetic Markers/genetics Haplotypes/genetics Heterozygote Humans Linkage Disequilibrium/genetics Male Nuclear Family Pedigree Polymorphism, Genetic/genetics Sample Size *Software Time FactorsApryComputational constraints currently limit exact multipoint linkage analysis to pedigrees of moderate size. We introduce new algorithms that allow analysis of larger pedigrees by reducing the time and memory requirements of the computation. We use the observed pedigree genotypes to reduce the number of inheritance patterns that need to be considered. The algorithms are implemented in a new version (version 2.1) of the software package GENEHUNTER. Performance gains depend on marker heterozygosity and on the number of pedigree members available for genotyping, but typically are 10-1,000-fold, compared with the performance of the previous release (version 2.0). As a result, families with up to 30 bits of inheritance information have been analyzed, and further increases in family size are feasible. In addition to computation of linkage statistics and haplotype determination, GENEHUNTER can also perform single-locus and multilocus transmission/disequilibrium tests. We describe and implement a set of permutation tests that allow determination of empirical significance levels in the presence of linkage disequilibrium among marker loci.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11254453 0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.11254453lDivision of Human Biology, Fred Hutchison Cancer Research Center, Seattle, WA 98109, USA. markiano@fhcrc.org7~? Markon, K. E.2006eSemiparametric maximum likelihood variance component estimation using mixture moment structure models360-6Twin Res Hum Genet93Humans *Likelihood Functions *Models, Genetic Monte Carlo Method Phenotype *Social Alienation *Twin Studies Twins, Dizygotic/genetics/psychology Twins, Monozygotic/genetics/psychologyJunNonnormal phenotypic distributions introduce significant problems in the estimation and selection of genetic models. Here, a semiparametric maximum likelihood approach to analyzing nonnormal phenotypes is described. In this approach, distributions are explicitly modeled together with genetic and environmental effects. Distributional parameters are introduced through mixture constraints, where the distribution of effects are discretized and freely estimated rather than assumed to be normal. Semiparametric maximum likelihood estimation can be used with a variety of genetic models, can be extended to a variety of pedigree structures, and has various advantages over other approaches to modeling nonnormal data.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16790146 !1832-4274 (Print) Journal Article16790146_Department of Psychology, University of Minnesota, Minneapolis, 55455, USA. mark0060@tc.umn.edu~?Marlow, A. J. Fisher, S. E. Francks, C. MacPhie, I. L. Cherny, S. S. Richardson, A. J. Talcott, J. B. Stein, J. F. Monaco, A. P. Cardon, L. R.2003PUse of multivariate linkage analysis for dissection of a complex cognitive trait561-70Am J Hum Genet723Analysis of Variance *Chromosome Mapping *Chromosomes, Human, Pair 18 *Chromosomes, Human, Pair 6 Cognition Disorders/*genetics Dyslexia/*genetics Humans Multivariate Analysis *Quantitative Trait LociMarReplication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12587094 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.12587094WWellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom. ~?6Martin, E. R. Monks, S. A. Warren, L. L. Kaplan, N. L.2000YA test for linkage and association in general pedigrees: the pedigree disequilibrium test146-54Am J Hum Genet671PAlleles Chromosome Mapping/*methods/*statistics & numerical data Computer Simulation Female Genetic Diseases, Inborn/epidemiology/genetics Genotype Humans Linkage Disequilibrium/*genetics Male Models, Genetic *Nuclear Family Pedigree Penetrance Prevalence Reproducibility of Results Research Design Sample Size Statistical DistributionsJulbFamily-based tests of linkage disequilibrium typically are based on nuclear-family data including affected individuals and their parents or their unaffected siblings. A limitation of such tests is that they generally are not valid tests of association when data from related nuclear families from larger pedigrees are used. Standard methods require selection of a single nuclear family from any extended pedigrees when testing for linkage disequilibrium. Often data are available for larger pedigrees, and it would be desirable to have a valid test of linkage disequilibrium that can use all potentially informative data. In this study, we present the pedigree disequilibrium test (PDT) for analysis of linkage disequilibrium in general pedigrees. The PDT can use data from related nuclear families from extended pedigrees and is valid even when there is population substructure. Using computer simulations, we demonstrated validity of the test when the asymptotic distribution is used to assess the significance, and examined statistical power. Power simulations demonstrate that, when extended pedigree data are available, substantial gains in power can be attained by use of the PDT rather than existing methods that use only a subset of the data. Furthermore, the PDT remains more powerful even when there is misclassification of unaffected individuals. Our simulations suggest that there may be advantages to using the PDT even if the data consist of independent families without extended family information. Thus, the PDT provides a general test of linkage disequilibrium that can be widely applied to different data structures.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10825280 !0002-9297 (Print) Journal Article10825280Center for Human Genetics, Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA. emartin@chg.mc.duke.edu~?!Martin, N. Boomsma, D. Machin, G.1997'A twin-pronged attack on complex traits387-92 Nat Genet174Diseases in Twins/*genetics Genetic Markers Humans Linkage (Genetics) Models, Genetic Multivariate Analysis *Quantitative Trait, Heritable Social Environment Twins, Dizygotic/*genetics Twins, Monozygotic/*geneticsDecJBefore one starts the hunt for quantitative trait loci (QTLs) for a complex trait, it is necessary to show that the trait is genetically influenced. This evidence is most likely to come from the classical twin study--the demonstration that monozygotic twins are more similar for the trait than dizygotic twins. The strengths and weaknesses of twin studies are discussed, and it is suggested that, far from becoming irrelevant with advances in molecular biology, they can improve the efficiency of QTL detection and play an important role in unravelling developmental genetic mechanisms.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9398838 (1061-4036 (Print) Journal Article Review9398838bQueensland Institute of Medical Research, PO Royal Brisbane Hospital, Australia. nickM@qimr.edu.au?&Martin, N.G. Boomsma, D.I. Neale, M.C.1989bGenetic analysis of twin and family data: structural modeling using LISREL. Foreword Special Issue5-7 Behav. Genet.19^~?Martin, N. G. Eaves, L. J.1977.The genetical analysis of covariance structure79-95Heredity381\Factor Analysis, Statistical Mathematics *Models, Biological Phenotype *Variation (Genetics)FebThe analysis of covariance structures (Joreskog, 1973) is adapted to the simultaneous maximum likelihood estimation of genetical and environmental factor loadings and specific variances. The goodness of fit is tested by chi square and standard errors of parameter estimates can be obtained. Any linear model used in univariate genetical analyses can be extended to the multivariate case. Most biological hypotheses about the relationships between variables can be specified by a variety of factor models. Individual parameters can be given fixed values or set to zero and hypotheses concerning the congruence of genetical and environmental correlations can be tested. The method is illustrated with published twin data on cognitive abilities.dhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=268313 !0018-067X (Print) Journal Article268313~?(Martin, N. G. Eaves, L. J. Fulker, D. W.1979eThe genetical relationship of impulsiveness and sensation seeking to Eysenck's personality dimensions197-210Acta Genet Med Gemellol (Roma)283Adolescent Adult Age Factors Aged Environment Extraversion (Psychology) Factor Analysis, Statistical Female Genetics, Behavioral Humans *Impulsive Behavior Male Middle Aged Models, Biological *Personality Personality Inventory Pregnancy Sex Factors Temperament *Twins/psychologyThe genetical analysis of covariance structures is used to explore the genetical and environmental intercorrelations of impulsiveness and sensation seeking factors and their conformity to Eysenck's principal personality dimensions. The independent dimensions of psychoticism, extraversion, neuroticism, and lie scale are not found to give a very satisfactory account of the genetical factor structure. In particular, it is clear that impulsiveness and sensation seeking are not simple reflections of extraversion.dhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=575838 30001-5660 (Print) Comparative Study Journal Article575838~?4Martin, N. G. Eaves, L. J. Kearsey, M. J. Davies, P.1978%The power of the classical twin study97-116Heredity401`Female Humans Mathematics *Models, Biological Pregnancy Probability *Twins *Variation (Genetics)Febdhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=272366 !0018-067X (Print) Journal Article272366? Maruyama, T.1982@Molecular Evolution, Protein Polymorphism and the Neutral Theory151-166'Japan Scientific Societies Press, TokyoTokyo Japan Scientific Societies Press_?Mather, K. Jinks, J.L1982Biometrical GeneticsLondonChapman and Hall?Matsueda, R.L Bielby, W.T19861Statistical power in covariance structure models.120-158Sociological MethodologyN .Brandon-TumaWashington, DC!American Sociological Association~?Matsuzaki, H. Loi, H. Dong, S. Tsai, Y. Y. Fang, J. Law, J. Di, X. Liu, W. M. Yang, G. Liu, G. Huang, J. Kennedy, G. C. Ryder, T. B. Marcus, G. A. Walsh, P. S. Shriver, M. D. Puck, J. M. Jones, K. W. Mei, R.2004hParallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonucleotide array414-25 Genome Res143Alleles Biological Markers Candidiasis, Chronic Mucocutaneous/genetics DNA Primers/*genetics DNA Probes/genetics/metabolism Ethnic Groups/genetics Genetic Predisposition to Disease/genetics Genome, Human Genotype Heterozygote Humans Linkage (Genetics)/genetics Oligonucleotide Array Sequence Analysis/*methods Polymorphism, Single Nucleotide/*genetics Reproducibility of Results Research Design/standards Thyroid Diseases/geneticsMarThe analysis of single nucleotide polymorphisms (SNPs) is increasingly utilized to investigate the genetic causes of complex human diseases. Here we present a high-throughput genotyping platform that uses a one-primer assay to genotype over 10,000 SNPs per individual on a single oligonucleotide array. This approach uses restriction digestion to fractionate the genome, followed by amplification of a specific fractionated subset of the genome. The resulting reduction in genome complexity enables allele-specific hybridization to the array. The selection of SNPs was primarily determined by computer-predicted lengths of restriction fragments containing the SNPs, and was further driven by strict empirical measurements of accuracy, reproducibility, and average call rate, which we estimate to be >99.5%, >99.9%, and>95%, respectively [corrected]. With average heterozygosity of 0.38 and genome scan resolution of 0.31 cM, the SNP array is a viable alternative to panels of microsatellites (STRs). As a demonstration of the utility of the genotyping platform in whole-genome scans, we have replicated and refined a linkage region on chromosome 2p for chronic mucocutaneous candidiasis and thyroid disease, previously identified using a panel of microsatellite (STR) markers.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14993208 !1088-9051 (Print) Journal Article149932085Affymetrix, Inc., Santa Clara, California 95051, USA. ~?McDermid, H. E. Morrow, B. E.2002Genomic disorders on 22q111077-88Am J Hum Genet705Animals *Chromosome Aberrations Chromosome Breakage/genetics Chromosomes, Human, Pair 22/*genetics Disease Models, Animal Genetic Diseases, Inborn/*genetics Genome, Human Humans Recombination, Genetic/genetics Syndrome T-Box Domain Proteins/geneticsMayNThe 22q11 region is involved in chromosomal rearrangements that lead to altered gene dosage, resulting in genomic disorders that are characterized by mental retardation and/or congenital malformations. Three such disorders-cat-eye syndrome (CES), der(22) syndrome, and velocardiofacial syndrome/DiGeorge syndrome (VCFS/DGS)-are associated with four, three, and one dose, respectively, of parts of 22q11. The critical region for CES lies centromeric to the deletion region of VCFS/DGS, although, in some cases, the extra material in CES extends across the VCFS/DGS region. The der(22) syndrome region overlaps both the CES region and the VCFS/DGS region. Molecular approaches have revealed a set of common chromosome breakpoints that are shared between the three disorders, implicating specific mechanisms that cause these rearrangements. Most VCFS/DGS and CES rearrangements are likely to occur by homologous recombination events between blocks of low-copy repeats (e.g., LCR22), whereas nonhomologous recombination mechanisms lead to the constitutional t(11;22) translocation. Meiotic nondisjunction events in carriers of the t(11;22) translocation can then lead to offspring with der(22) syndrome. The molecular basis of the clinical phenotype of these genomic disorders has also begun to be addressed. Analysis of both the genomic sequence for the 22q11 interval and the orthologous regions in the mouse has identified >24 genes that are shared between VCFS/DGS and der(22) syndrome and has identified 14 putative genes that are shared between CES and der(22) syndrome. The ability to manipulate the mouse genome aids in the identification of candidate genes in these three syndromes. Research on genomic disorders on 22q11 will continue to expand our knowledge of the mechanisms of chromosomal rearrangements and the molecular basis of their phenotypic consequences.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11925570 (0002-9297 (Print) Journal Article Review11925570TDepartment of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.~?$McGinnis, R. Shifman, S. Darvasi, A.2002MPower and efficiency of the TDT and case-control design for association scans135-44 Behav Genet322Case-Control Studies Cost-Benefit Analysis Gene Frequency/*genetics Genetic Markers/*genetics Genetic Screening/*economics Genotype Humans Linkage Disequilibrium/*genetics Risk Sampling StudiesMarSample size required for the TDT and the case-control designs was studied for marker-based genome-wide scans for disease association. The influence of various parameters on sample size required to attain a given level of power was analyzed in detail. Small genotypic relative risks, low levels of linkage disequilibrium, and departure from equal frequencies for the disease allele and associated marker allele, significantly and similarly increase sample size required by either the TDT or case-control design. Under the case-control paradigm, we show that the optimal strategy will often be to collect many more control individuals than disease cases with the optimal ratio depending on the relative cost of acquiring cases as compared to controls. For the TDT, the number of required simplex families is virtually equal to the number of cases required for similar power in case-control studies with an equal number of cases and controls. The case-control approach may therefore prove to be more economical and expeditious than the TDT design for diseases in which the cost and time required to collect simplex families is much greater than that needed to acquire isolated disease cases. Nevertheless, possible population stratification needs to be addressed when the case-control design is applied.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12036111 g0001-8244 (Print) Comparative Study Evaluation Studies Journal Article Research Support, Non-U.S. Gov't12036111YGlaxoSmithKline Pharmaceuticals, Harlow, Essex, United Kingdom. Ralph_McGinnis@sbphrd.com5~?McGrath, J. Solter, D.1984QCompletion of mouse embryogenesis requires both the maternal and paternal genomes179-83Cell371Animals Cell Nucleus/*physiology Diploidy Embryo/*physiology Female *Genes Genotype Male Mice Mice, Inbred BALB C Mice, Inbred C57BL Pregnancy PseudopregnancyMay/Transplantation of pronuclei between one-cell-stage embryos was used to construct diploid mouse embryos with two female pronuclei ( biparental gynogenones ) or two male pronuclei ( biparental androgenones ). The ability of these embryos to develop to term was compared with control nuclear-transplant embryos in which the male or the female pronucleus was replaced with an isoparental pronucleus from another embryo. The results show that diploid biparental gynogenetic and androgenetic embryos do not complete normal embryogenesis, whereas control nuclear transplant embryos do. We conclude that the maternal and paternal contributions to the embryonic genome in mammals are not equivalent and that a diploid genome derived from only one of the two parental sexes is incapable of supporting complete embryogenesis.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6722870 O0092-8674 (Print) In Vitro Journal Article Research Support, U.S. Gov't, P.H.S.6722870 :~?McKeigue, P. M.1997uMapping genes underlying ethnic differences in disease risk by linkage disequilibrium in recently admixed populations188-96Am J Hum Genet601Alleles *Chromosome Mapping Ethnic Groups/*genetics Genetic Markers *Genetic Predisposition to Disease Genetic Techniques Genetics, Medical Heterozygote Humans *Linkage Disequilibrium Risk Factors Sample Size Statistics/methodsJanXWhere recent admixture has occurred between two populations that have different disease rates for genetic reasons, family-based association studies can be used to map the genes underlying these differences, if the ancestry of the alleles at each locus examined can be assigned to one of the two founding populations. This article explores the statistical power and design requirements of this approach. Markers suitable for assigning the ancestry of genomic regions could be defined by grouping alleles at closely spaced microsatellite loci into haplotypes, or generated by representational difference analysis. For a given relative risk between populations, the sample size required to detect a disease locus that accounts for this relative risk by linkage-disequilibrium mapping in an admixed population is not critically dependent on assumptions about genotype penetrances or allele frequencies. Using the transmission-disequilibrium test to search the genome for a locus that accounts for a relative risk of between 2 and 3 in a high-risk population, compared with a low-risk population, generally requires between 150 and 800 case-parent pairs of mixed descent. The optimal strategy is to conduct an initial study using markers spaced at < or = 10 cM with cases from the second and third generations of mixed descent, and then to map the disease loci more accurately in a subsequent study of a population with a longer history of admixture. This approach has greater statistical power than allele-sharing designs and has obvious applications to the genetics of hypertension, non-insulin-dependent diabetes, and obesity.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8981962 !0002-9297 (Print) Journal Article8981962Epidemiology Unit, Department of Epidemiology and Population Sciences, London School of Hygiene and Tropical Medicine, United Kingdom. pmckeigu@lshtm.ac.uk Q~?McKenzie, C. A. Abecasis, G. R. Keavney, B. Forrester, T. Ratcliffe, P. J. Julier, C. Connell, J. M. Bennett, F. McFarlane-Anderson, N. Lathrop, G. M. Cardon, L. R.2001mTrans-ethnic fine mapping of a quantitative trait locus for circulating angiotensin I-converting enzyme (ACE)1077-84 Hum Mol Genet1010LAfrican Continental Ancestry Group/genetics Chromosome Mapping European Continental Ancestry Group/genetics Female Genetic Markers Genotype Haplotypes/genetics Humans Jamaica Linkage (Genetics) Linkage Disequilibrium Male Models, Biological Peptidyl-Dipeptidase A/blood/*genetics Polymorphism, Genetic *Quantitative Trait, HeritableMay 1\Circulating angiotensin I-converting enzyme (ACE) levels are influenced by a major quantitative trait locus (QTL) that maps to the ACE gene. Phylogenetic and measured haplotype analyses have suggested that the ACE-linked QTL lies downstream of a putative ancestral breakpoint located near to position 6435. However, strong linkage disequilibrium between markers in the 3' portion of the gene has prevented further resolution of the QTL in Caucasian subjects. We have examined 10 ACE gene polymorphisms in Afro-Caribbean families recruited in JAMAICA: Variance components analyses showed strong evidence of linkage and association to circulating ACE levels. When the linkage results were contrasted with those from a set of British Caucasian families, there was no evidence for heterogeneity between the samples. However, patterns of allelic association between the markers and circulating ACE levels differed significantly in the two data sets. In the British families, three markers [G2215A, Alu insertion/deletion and G2350A] were in complete disequilibrium with the ACE-linked QTL. In the Jamaican families, only marker G2350A showed strong but incomplete disequilibrium with the ACE-linked QTL. These results suggest that additional unobserved polymorphisms have an effect on circulating ACE levels in Jamaican families. Furthermore, our results show that a variance components approach combined with structured, quantitative comparisons between families from different ethnic groups may be a useful strategy for helping to determine which, if any, variants in a small genomic region directly influence a quantitative trait.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11331618 g0964-6906 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11331618mTropical Metabolism Research Unit, University of the West Indies, Kingston 7, Jamaica. camcken@uwimona.edu.jm /~? McPeek, M. S.1999NOptimal allele-sharing statistics for genetic mapping using affected relatives225-49Genet Epidemiol163Alleles *Chromosome Mapping Genes, Dominant Humans Linkage (Genetics) Matched-Pair Analysis *Models, Genetic *Models, Statistical Multigene Family Pedigree Phenotype Quantitative Trait, HeritableeThe choice of allele-sharing statistics can have a great impact on the power of robust affected relative methods. Similarly, when allele-sharing statistics from several pedigrees are combined, the weight applied to each pedigree's statistic can affect power. Here we describe the direct connection between the affected relative methods and traditional parametric linkage analysis, and we use this connection to give explicit formulae for the optimal sharing statistics and weights, applicable to all pedigree types. One surprising consequence is that under any single gene model, the value of the optimal allele-sharing statistic does not depend on whether observed sharing is between more closely or more distantly related affected relatives. This result also holds for any multigene model with loci unlinked, additivity between loci, and all loci having small effect. For specific classes of two-allele models, we give the most powerful statistics and optimal weights for arbitrary pedigrees. When the effect size is small, these also extend to multigene models with additivity between loci. We propose a useful new statistic, S(rob dom), which performs well for dominant and additive models with varying phenocopy rates and varying predisposing allele frequency. We find that the statistic S(_#alleles), performs well for recessive models with varying phenocopy rates and varying redisposing allele frequency. We also find that for models with large deviation from null sharing, the correspondence between allele-sharing statistics and the models for which they are optimal may also depend on which method is used to test for linkage.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10096687 0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.10096687`Department of Statistics, University of Chicago, Illinois 60637, USA. mcpeek@galton.uchicago.edu ~?McPeek, M. S. Sun, L.2000ZStatistical tests for detection of misspecified relationships by use of genome-screen data1076-94Am J Hum Genet663LAlcoholism/genetics Alleles Chromosome Mapping/*methods/statistics & numerical data Computer Simulation Consanguinity Female Gene Frequency/genetics *Genetic Screening *Genome, Human Genotype Humans Likelihood Functions Male Markov Chains Matched-Pair Analysis Microsatellite Repeats/genetics Models, Genetic Nuclear Family PedigreeMarbMisspecified relationships can have serious consequences for linkage studies, resulting in either reduced power or false-positive evidence for linkage. If some individuals in the pedigree are untyped, then Mendelian errors may not be observed. Previous approaches to detection of misspecified relationships by use of genotype data were developed for sib and half-sib pairs. We extend the likelihood calculations of Goring and Ott and Boehnke and Cox to more-general relative pairs, for which identity-by-descent (IBD) status is no longer a Markov chain, and we propose a likelihood-ratio test. We also extend the identity-by-state (IBS)-based test of Ehm and Wagner to nonsib relative pairs. The likelihood-ratio test has high power, but its drawbacks include the need to construct and apply a separate Markov chain for each possible alternative relationship and the need for simulation to assess significance. The IBS-based test is simpler but has lower power. We propose two new test statistics-conditional expected IBD (EIBD) and adjusted IBS (AIBS)-designed to retain the simplicity of IBS while increasing power by taking into account chance sharing. In simulations, the power of EIBD is generally close to that of the likelihood-ratio test. The power of AIBS is higher than that of IBS, in all cases considered. We suggest a strategy of initial screening by use of EIBD and AIBS, followed by application of the likelihood-ratio test to only a subset of relative pairs, identified by use of EIBD and AIBS. We apply the methods to a Genetic Analysis Workshop 11 data set from the Collaborative Study on the Genetics of Alcoholism.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10712219 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10712219dDepartment of Statistics, University of Chicago, Chicago, IL, 60637, USA. mcpeek@galton.uchicago.eduT~?Medland, S. E.2004PAlternate parameterization for scalar and non-scalar sex-limitation models in Mx299-305Twin Res73iAlgorithms Female Humans Male *Models, Theoretical Sex Factors *Twins Twins, Dizygotic Twins, MonozygoticJunIThe purpose of this article is to present alternative parameterizations of scalar and non-scalar sexlimitation models in the Mx matrix algebra program (Neale et al., 2002). These models are designed for use with extended pedigrees and take advantage of the dynamic treatment of covariates within Mx. Example scripts are provided.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15193175 31369-0523 (Print) Comparative Study Journal Article15193175VQueensland Institute of Medical Research, Brisbane QLD, Australia. sarahMe@qimr.edu.au~?Medland, S. E.2005AParameterization of sex-limited autosomal linkage analysis for Mx569-73Twin Res Hum Genet86dHumans *Models, Genetic Quantitative Trait Loci/*genetics *Quantitative Trait, Heritable Sex FactorsDecWIncorporation of sex-limitation (genotype-sex interaction) effects into a model of quantitative trait loci ( QTL) analysis has been shown to increase the power to detect linkage when analyzing traits in which sex limitation is present (Towne et al., 1997). The present note provides a parameterization of the nonscalar sex-limitation ACE model incorporating autosomal sex-limited QTL effects for use with the Mx matrix algebra program (Neale et al., 2002). An example script designed for use with extended sibships that takes advantage of the versatile treatment of covariates within Mx is included.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16354498 B1832-4274 (Print) Journal Article Research Support, Non-U.S. Gov't16354498tQueensland Institute of Medical Research, PO Royal Brisbane Hospital, Brisbane, Australia. Sarah.Medland@qimr.edu.au?Meeker, W.Q. Escobar, L.A1995TTeaching about approximate confidence regions based on maximum likelihood estimation48-53 Am. Stat.49z~?JMichalatos-Beloin, S. Tishkoff, S. A. Bentley, K. L. Kidd, K. K. Ruano, G.1996VMolecular haplotyping of genetic markers 10 kb apart by allele-specific long-range PCR4841-3Nucleic Acids Res2423*Alleles Antigens, CD4/*genetics Gene Deletion *Genetic Markers Genotype *Haplotypes Humans Linkage (Genetics) Polymerase Chain Reaction/*methods Repetitive Sequences, Nucleic AcidDec 1Haplotypes, combinations of polymorphic markers in a chromosome, are critical for genome diversity research. However, their utility in population samplings is compromised by uncertain linkage phase determinations from unrelated individuals. Molecular haplotyping accomplishes direct phase determination by generation of hemizygous templates from diploid genomic samples. We report molecular haplotyping by allele-specific long-range PCR of two markers 9.5 kb apart at the CD4 locus: a bi-allelic Alu deletion and a multi-allelic repeat. We verified CD4 molecular haplotypes by classical Mendelian analysis. Molecular haplotyping should prove useful in mapping disease genes and in establishing founder effects.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8972876 o0305-1048 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.8972876,BIOS Laboratories, New Haven, CT 06511, USA.~? Migeon, B. R.2002/X chromosome inactivation: theme and variations8-16Cytogenet Genome Res991-4Animals *Dosage Compensation, Genetic Evolution, Molecular Humans RNA, Untranslated/genetics Variation (Genetics) X Chromosome/*geneticseMy contribution to this special issue on Vertebrate Sex Chromosomes deals with the theme of X chromosome inactivation and its variations. I will argue that the single active X--characteristic of mammalian X dosage compensation--is unique to mammals, and that the major underlying mechanism(s) must be the same for most of them. The variable features reflect modifications that do not interfere with the basic theme. These variations were acquired during mammalian evolution--to solve special needs for imprinting and locking in the inactive state. Some of the adaptations reinforce the basic theme, and were needed because of species differences in the timing of interacting developmental events. Elucidating the molecular basis for the single active X requires that we distinguish the mechanisms essential for the basic theme from those responsible for its variations.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12900539 R1424-859X (Electronic) Journal Article Research Support, U.S. Gov't, P.H.S. Review12900539McKusick Nathans Institute of Genetic Medicine and Department of Pediatrics, The Johns Hopkins University School of Medicine, Baltimore MD, USA. bmigeon@jhmi.edu?Miller, I. Miller M.2004FJohn E. Freund’s Mathematical Statistics with Applications, 7th Edn.Upper Saddle River, NJ.Pearson Education International ~?-Mitchell, A. A. Cutler, D. J. Chakravarti, A.2003vUndetected genotyping errors cause apparent overtransmission of common alleles in the transmission/disequilibrium test598-610Am J Hum Genet723Alleles *Genotype Humans *Linkage Disequilibrium Models, Genetic Models, Statistical *Mutation Reproducibility of Results Variation (Genetics)MarThe transmission/disequilibrium test (TDT), a family-based test of linkage and association, is a popular and intuitive statistical test for studies of complex inheritance, as it is nonparametric and robust to population stratification. We carried out a literature search and located 79 significant TDT-derived associations between a microsatellite marker allele and a disease. Among these, there were 31 (39%) in which the most common allele was found to exhibit distorted transmission to affected offspring, implying that the allele may be associated with either susceptibility to or protection from a disease. In 27 of these 31 studies (87%), the most common allele appeared to be overtransmitted to affected offspring (a risk factor), and, in the remaining 4 studies, the most common allele appeared to be undertransmitted (a protective factor). In a second literature search, we identified 92 case-control studies in which a microsatellite marker allele was found to have significantly different frequencies in case and control groups. Of these, there were 37 instances (40%) in which the most common allele was involved. In 12 of these 37 studies (32%), the most common allele was enriched in cases relative to controls (a risk factor), and, in the remaining 25 studies, the most common allele was enriched in controls (a protective factor). Thus, the most common allele appears to be a risk factor when identified through the TDT, and it appears to be protective when identified through case-control analysis. To understand this phenomenon, we incorporated an error model into the calculation of the TDT statistic. We show that undetected genotyping error can cause apparent transmission distortion at markers with alleles of unequal frequency. We demonstrate that this distortion is in the direction of overtransmission for common alleles. Therefore, we conclude that undetected genotyping errors may be contributing to an inflated false-positive rate among reported TDT-derived associations and that genotyping fidelity must be increased.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12587097 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.12587097cMcKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21287, USA.~?#Molitor, J. Marjoram, P. Thomas, D.2003]Fine-scale mapping of disease genes with multiple mutations via spatial clustering techniques1368-84Am J Hum Genet736Algorithms Ataxia/genetics Bayes Theorem Cluster Analysis Cystic Fibrosis/genetics Genetic Diseases, Inborn/*genetics Haplotypes/*genetics Humans Markov Chains *Models, Genetic Monte Carlo Method Mutation/geneticsDecWe present a method to perform fine mapping by placing haplotypes into clusters on the basis of risk. Each cluster has a haplotype "center." Cluster allocation is defined according to haplotype centers, with each haplotype assigned to the cluster with the "closest" center. The closeness of two haplotypes is determined by a similarity metric that measures the length of the shared segment around the location of a putative functional mutation for the particular cluster. Our method allows for missing marker information but still estimates the risks of complete haplotypes without resorting to a one-marker-at-a-time analysis. The dimensionality issues that can occur in haplotype analyses are removed by sampling over the haplotype space, allowing for estimation of haplotype risks without explicitly assigning a parameter to each haplotype to be estimated. In this way, we are able to handle haplotypes of arbitrary size. Furthermore, our clustering approach has the potential to allow us to detect the presence of multiple functional mutations.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14631555 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.14631555Department of Preventive Medicine, University of Southern California, Los Angeles, Los Angeles, CA, 90089, USA. jmolitor@usc.edu&~?#Molitor, J. Marjoram, P. Thomas, D.2003fApplication of Bayesian spatial statistical methods to analysis of haplotypes effects and gene mapping95-105Genet Epidemiol252*Bayes Theorem Chromosome Mapping/*statistics & numerical data Cystic Fibrosis/genetics Friedreich Ataxia/genetics Haplotypes/*genetics Humans Markov Chains Models, Genetic Monte Carlo MethodSepaWe propose a method to analyze haplotype effects using ideas derived from Bayesian spatial statistics. We assume that two haplotypes that are similar to one another in structure are likely to have similar risks, and define a distance metric to specify the appropriate level of closeness between the two haplotypes. Through the choice of distance metric, varying levels of population genetics theory can be incorporated into the modeling process, including some that allow estimation of the location of the disease causing mutation(s). This location can be estimated, along with the other parameters of the model, using Markov chain Monte Carlo (MCMC) estimation methods. We demonstrate the effectiveness of the model on two real datasets, a well-known dataset used to fine-map the gene for cystic fibrosis, and one used to localize the gene for Friedreich's ataxia.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12916018 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.12916018uDepartment of Preventive Medicine, University of Southern California, Los Angeles, 900089-9011, USA. jmolitor@usc.edu~?!Molitor, J. Zhao, K. Marjoram, P.2005!Fine mapping - 19th century styleS63 BMC Genet 6 Suppl 1Dec 30HABSTRACT : BACKGROUND : There is great interest in the use of computationally intensive methods for fine mapping of marker data. In this paper we develop methods based upon ideas originally proposed 100 years ago in the context of spatial clustering. METHODS : We use spatial clustering of haplotypes as a low-dimensional surrogate for the unobserved genealogy underlying a set of genotype data. In doing so we hope to avoid the computational complexity inherent in explicitly modelling details of the ancestry of the sample, while at the same time capturing the key correlations induced by that ancestry at a much lower computational cost. RESULTS : We benchmark our methods using the simulated Genetic Analysis Workshop 14 data, using 100 replicates of 4 phenotypes to indicate the power of our method. When a functional mutation relating to a trait is actually present, we find evidence for that mutation in 97 out of 100 replicates, on average. CONCLUSION : Our results show that our method has the ability to accurately infer the location of functional mutations from unphased genotype data.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16451676 &1471-2156 (Electronic) Journal article16451676xDepartment of Preventive Medicine, University of Southern California, Los Angeles, CA 90089-9011, USA. jmolitor@usc.edu. ~?6Monks, S. A. Martin, E. R. Umbach, D. M. Kaplan, N. L.1999{Two tests of association for a susceptibility locus for families of variable size: an example using two sampling strategiesS655-60Genet Epidemiol 17 Suppl 1Environment Family Genetic Markers *Genetic Predisposition to Disease Genetic Screening Humans Linkage (Genetics) Linkage Disequilibrium *Models, Genetic Monte Carlo Method SoftwareA two-stage approach was used to analyze Problem 2 simulated data from Genetic Analysis Workshop 11. In the first stage, we tested for linkage with the Haseman-Elston test in SIBPAL. Markers that were significant in the first stage were followed up with two types of association tests. These association tests differ in the type of family information used: 1) parental transmissions to affected children or 2) differences in marker allele frequencies between affected and unaffected siblings. We also explored how the conclusions changed when different sampling strategies were used. Of particular interest was whether the entire data set should be used to test for both linkage and association or whether the data set should be halved to allow for replication of the initial association results.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10597509 0741-0395 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.10597509HDepartment of Statistics, North Carolina State University, Raleigh, USA.~?Moore, T. Haig, D.1991BGenomic imprinting in mammalian development: a parental tug-of-war45-9 Trends Genet72lAnimals Embryonic and Fetal Development/genetics *Evolution *Gene Expression Humans X Chromosome/*metabolismFebsGenomic imprinting in mammals is increasingly being implicated in developmental and pathological processes, but without a clear understanding of its function in normal development. We believe that imprinting has evolved in mammals because of the conflicting interests of maternal and paternal genes in relation to the transfer of nutrients from the mother to her offspring. We present an hypothesis that accounts for many of the observed effects of imprinting in mammals and relates them to similar observations in plants. This hypothesis has implications for studies of X-chromosome inactivation and a range of human diseases.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2035190 I0168-9525 (Print) Journal Article Research Support, Non-U.S. Gov't Review2035190aMRC Experimental Embryology and Teratology Unit, St George's Hospital Medical School, London, UK.U? Morgan, T.H.1910Chromosomes and heredity449=496Am. Nat44~?+Morison, I. M. Ramsay, J. P. Spencer, H. G.2005 A census of mammalian imprinting457-65 Trends Genet218Animals Evolution, Molecular Female *Genomic Imprinting Humans Introns Male Mice Models, Genetic RNA, Untranslated/genetics Retroelements/genetics Species Specificity Transcription, GeneticAug;Genomic imprinting, the parent-of-origin-specific silencing of a small proportion of genes, introduces a paradoxical vulnerability of hemizygosity into the diploid mammalian genome. To facilitate the evaluation of the biological and evolutionary significance of imprinting, we have collated a census of known imprinted genes, listing 83 transcriptional units of which 29 are imprinted in both humans and mice. There is a high level of discordance of imprinting status between the mouse and human, even when cases in which the orthologue is absent from one species are excluded. A high proportion of imprinted genes are noncoding RNAs or genes derived by retrotransposition. Accumulation of functional and comparative data for these genes will improve our understanding of imprinting and its contribution to mammalian evolution.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15990197 [0168-9525 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Review15990197Cancer Genetics Laboratory, Department of Biochemistry and National Research Centre for Growth and Development, University of Otago, PO Box 56, Dunedin, New Zealand. ian.morison@otago.ac.nz~?vMorley, K. I. Medland, S. E. Ferreira, M. A. Lynskey, M. T. Montgomery, G. W. Heath, A. C. Madden, P. A. Martin, N. G.2006A possible smoking susceptibility locus on chromosome 11p12: evidence from sex-limitation linkage analyses in a sample of Australian twin families87-99 Behav Genet361Adult Aged Aged, 80 and over Analysis of Variance Chromosome Mapping *Chromosomes, Human, Pair 11 Female *Genetic Predisposition to Disease Humans Male Middle Aged Phenotype Queensland Questionnaires Smoking/*genetics Tobacco Use Disorder/genetics Twins, Dizygotic Twins, MonozygoticJanMany twin studies have identified sex differences in the influence of genetic and environmental factors on smoking behaviors. We explore the evidence for sex differences for smoking initiation and cigarette consumption in a sample of Australian twin families, and extend these models to incorporate sex differences in linkage analyses for these traits. We further examine the impact of including or excluding non-smokers in genetic analyses of tobacco consumption. Accounting for sex differences improved linkage results in some instances. We identified one region suggestive of linkage on chromosome 11p12. This locus, as well as another region identified on chromosome 6p12, replicates regions identified in previous studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16365831 r0001-8244 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Twin Study16365831Genetic Epidemiology, Queensland Institute of Medical Research, PO Royal Brisbane Hospital, Brisbane, Queensland, Australia. kateM@qimr.edu.auP~?^Morley, M. Molony, C. M. Weber, T. M. Devlin, J. L. Ewens, K. G. Spielman, R. S. Cheung, V. G.2004BGenetic analysis of genome-wide variation in human gene expression743-7Nature4307001PAlleles B-Lymphocytes/metabolism *Gene Expression Profiling *Gene Expression Regulation *Genome, Human *Genomics Genotype Humans Linkage (Genetics) Oligonucleotide Array Sequence Analysis Phenotype Polymorphism, Single Nucleotide/genetics RNA, Messenger/genetics/metabolism Transcription, Genetic/genetics Variation (Genetics)/*geneticsAug 12Natural variation in gene expression is extensive in humans and other organisms, and variation in the baseline expression level of many genes has a heritable component. To localize the genetic determinants of these quantitative traits (expression phenotypes) in humans, we used microarrays to measure gene expression levels and performed genome-wide linkage analysis for expression levels of 3,554 genes in 14 large families. For approximately 1,000 expression phenotypes, there was significant evidence of linkage to specific chromosomal regions. Both cis- and trans-acting loci regulate variation in the expression levels of genes, although most act in trans. Many gene expression phenotypes are influenced by several genetic determinants. Furthermore, we found hotspots of transcriptional regulation where significant evidence of linkage for several expression phenotypes (up to 31) coincides, and expression levels of many genes that share the same regulatory region are significantly correlated. The combination of microarray techniques for phenotyping and linkage analysis for quantitative traits allows the genetic mapping of determinants that contribute to variation in human gene expression.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15269782 l1476-4687 (Electronic) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15269782Department of Pediatrics, University of Pennsylvania, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA. ~? Morris, A. P.2005Direct analysis of unphased SNP genotype data in population-based association studies via Bayesian partition modelling of haplotypes91-107Genet Epidemiol292Algorithms Alleles *Bayes Theorem Cytochrome P-450 CYP2D6/*genetics Genetic Predisposition to Disease Genotype Haplotypes/*genetics Humans Logistic Models Markov Chains *Models, Genetic Monte Carlo Method Polymorphism, Single Nucleotide/*genetics Risk AssessmentSepWe describe a novel method for assessing the strength of disease association with single nucleotide polymorphisms (SNPs) in a candidate gene or small candidate region, and for estimating the corresponding haplotype relative risks of disease, using unphased genotype data directly. We begin by estimating the relative frequencies of haplotypes consistent with observed SNP genotypes. Under the Bayesian partition model, we specify cluster centres from this set of consistent SNP haplotypes. The remaining haplotypes are then assigned to the cluster with the "nearest" centre, where distance is defined in terms of SNP allele matches. Within a logistic regression modelling framework, each haplotype within a cluster is assigned the same disease risk, reducing the number of parameters required. Uncertainty in phase assignment is addressed by considering all possible haplotype configurations consistent with each unphased genotype, weighted in the logistic regression likelihood by their probabilities, calculated according to the estimated relative haplotype frequencies. We develop a Markov chain Monte Carlo algorithm to sample over the space of haplotype clusters and corresponding disease risks, allowing for covariates that might include environmental risk factors or polygenic effects. Application of the algorithm to SNP genotype data in an 890-kb region flanking the CYP2D6 gene illustrates that we can identify clusters of haplotypes with similar risk of poor drug metaboliser (PDM) phenotype, and can distinguish PDM cases carrying different high-risk variants. Further, the results of a detailed simulation study suggest that we can identify positive evidence of association for moderate relative disease risks with a sample of 1,000 cases and 1,000 controls.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15940704 B0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't15940704WWellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom. ~? Morris, A. P.2006A flexible Bayesian framework for modeling haplotype association with disease, allowing for dominance effects of the underlying causative variants679-94Am J Hum Genet794Algorithms Alleles *Bayes Theorem Cytochrome P-450 CYP2D6/genetics *Genes, Dominant *Genetic Predisposition to Disease Haplotypes Humans Linkage Disequilibrium Logistic Models Models, Genetic *Polymorphism, Single NucleotideOct)Multilocus analysis of single-nucleotide-polymorphism (SNP) haplotypes may provide evidence of association with disease, even when the individual loci themselves do not. Haplotype-based methods are expected to outperform single-SNP analyses because (i) common genetic variation can be structured into haplotypes within blocks of strong linkage disequilibrium and (ii) the functional properties of a protein are determined by the linear sequence of amino acids corresponding to DNA variation on a haplotype. Here, I propose a flexible Bayesian framework for modeling haplotype association with disease in population-based studies of candidate genes or small candidate regions. I employ a Bayesian partition model to describe the correlation between marker-SNP haplotypes and causal variants at the underlying functional polymorphism(s). Under this model, haplotypes are clustered according to their similarity, in terms of marker-SNP allele matches, which is used as a proxy for recent shared ancestry. Haplotypes within a cluster are then assigned the same probability of carrying a causal variant at the functional polymorphism(s). In this way, I can account for the dominance effect of causal variants, here corresponding to any deviation from a multiplicative contribution to disease risk. The results of a detailed simulation study demonstrate that there is minimal cost associated with modeling these dominance effects, with substantial gains in power over haplotype-based methods that do not incorporate clustering and that assume a multiplicative model of disease risks.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16960804 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't16960804`Wellcome Trust Centre for Human Genetics, Oxford, OX3 7BN, United Kingdom. amorris@well.ox.ac.uk ]~?Morris, R. W. Kaplan, N. L.2002aOn the advantage of haplotype analysis in the presence of multiple disease susceptibility alleles221-33Genet Epidemiol233*Alleles Case-Control Studies Chromosome Mapping/statistics & numerical data Gene Frequency/genetics Genetic Diseases, Inborn/*genetics Genetic Markers/genetics Genetic Predisposition to Disease/*genetics Genetic Screening/statistics & numerical data Haplotypes/*genetics Humans Likelihood Functions Linkage Disequilibrium/genetics Polymorphism, Single Nucleotide/genetics Reproducibility of Results Risk Assessment/statistics & numerical dataOctWe investigated the effect of multiple susceptibility alleles at a single disease locus on the statistical power of a likelihood ratio test to detect association between alleles at a marker locus and a disease phenotype in a case-control design. Using simplifying assumptions to obtain the joint frequency distribution of marker and disease locus alleles, we present numerical results that illustrate the impact of historical variation of initial associations between marker alleles and susceptibility alleles on the power of a likelihood ratio test for association. Our results show that an increase in the number of susceptibility alleles produces a decrease in power of the likelihood ratio test. The decrease in power in the presence of multiple susceptibility alleles, however, is less for markers with multiple alleles than for markers with two alleles. We investigate the implications of this observation for tests of association based on haplotypes made up of tightly linked single-nucleotide polymorphisms (SNPs). Our results suggest that an analysis based on haplotypes can be advantageous over an analysis based on individual SNPs in the presence of multiple susceptibility alleles, particularly when linkage disequilibria between SNPs is weak. The results provide motivation for further development of statistical methods based on haplotypes for assessing the potential for association methods to identify and locate complex disease genes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12384975 !0741-0395 (Print) Journal Article12384975Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709-2233, USA. morrisr@niehs.nih.gov~? Morton, N. E.1955-Sequential tests for the detection of linkage277-318Am J Hum Genet73 *GeneticsSepfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=13258560 !0002-9297 (Print) Journal Article13258560N~?2Mosley, J. Conti, D. V. Elston, R. C. Witte, J. S.2001}Impact of preadjusting a quantitative phenotype prior to sib-pair linkage analysis when gene x environment interaction existsS837-42Genet Epidemiol 21 Suppl 1Chromosome Mapping/*statistics & numerical data Environmental Exposure/*adverse effects Genetic Predisposition to Disease/*genetics *Genotype Humans Lod Score *Models, Genetic Phenotype *Quantitative Trait, Heritable Regression Analysis.The investigation of potential gene x environment (G x E) interactions is an important facet in the study of complex diseases. When G x E interaction exists, linkage analyses of the interacting gene must treat the environmental factor appropriately. Specifically, the common approach of regressing out an environmental factor prior to linkage analysis may be inappropriate if that factor has an interaction with the gene. This is explored here in the Genetic Analysis Workshop 12 simulated data set using the G x E interaction between major gene four (MG4) and environmental factor two (E2). The analysis shows that preadjusting the quantitative trait three (Q3) phenotype for the main effects of several environmental variables, including one (E2) that interacts with MG4, affects the results of a Haseman-Elston linkage analysis. In particular, the agreement in detecting linkage between preadjusting versus not preadjusting was only 78% and 66% using alpha levels of 0.05 and 0.10, respectively. For both approaches, incorporating an interaction term in the regression models enabled linkage to be detected where the evidence was either minimal or not present in an identical-by-descent main effects-only model. Furthermore, preadjustment for E2 did not appear to account for the major discrepancies between the approaches.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11793789 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11793789~Department of Epidemiology and Biostatistics, Case Western Reserve University, 2500 MetroHealth Dr., Cleveland, OH 44109, USA.~?Mote, V. L. Anderson, R. L.1965xAn Investigation of the Effect of Misclassification on the Properties of Chi-2-Tests in the Analysis of Categorical Data95-109 Biometrika52*Mathematics *StatisticsJunfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14341284 !0006-3444 (Print) Journal Article14341284~?Mukhopadhyay, N. Weeks, D. E.2003\Linkage analysis of adult height with parent-of-origin effects in the Framingham Heart StudyS76 BMC Genet 4 Suppl 1Adult Adult Children Body Height/*genetics Chromosome Mapping/statistics & numerical data Chromosomes, Human, Pair 14/genetics Chromosomes, Human, Pair 18/genetics Chromosomes, Human, Pair 19/genetics Cohort Studies Epigenesis, Genetic/genetics Female Genomic Imprinting/*genetics Humans *Linkage (Genetics) Longitudinal Studies Male Middle Aged Phenotype Regression Analysis Software/statistics & numerical dataCurrent linkage analysis methods for quantitative traits do not usually incorporate imprinting effects. Here, we carried out genome-wide linkage analysis for loci influencing adult height in the Framingham Heart Study subjects using variance components while allowing for imprinting effects. We used a sex-averaged map for the 22 autosomes, while chromosomes 6, 14, 18, and 19 were also analyzed using sex-specific maps. We compared results from these four analyses: 1) non-imprinted with sex-averaged maps, 2) imprinted with sex-averaged maps, 3) non-imprinted with sex-specific maps, and 4) imprinted with sex-specific maps. We found four regions on three chromosomes (14q32, 18p11-q21, 18q21-22, and 19q13) with LOD scores above 2.0, with a maximum LOD score of 3.12, allowing for imprinting and sex-specific maps, at D18S1364 on 18q21. While we obtained significant evidence of imprinting effects in both the 18p11-q21 and 19q13 regions when using sex-averaged maps, there were no significant differences between the imprinted and non-imprinted LOD scores when we used sex-specific maps. Our results illustrate the importance of allowing for gender-specific effects in linkage analyses, whether these are in the form of gender-specific recombination frequencies, or in the form of imprinting effects.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14975144 ~1471-2156 (Electronic) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.14975144Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. nandita@pitt.edu>~?IMurphy, V. E. Mynett-Johnson, L. A. Claffey, E. Shields, D. C. McKeon, P.2001UNo association between 5HT-2A and bipolar disorder irrespective of genomic imprinting422-5Am J Med Genet1055Alleles Bipolar Disorder/*genetics DNA/genetics Family Health Female Gene Frequency *Genomic Imprinting Genotype Haplotypes Humans Male Receptor, Serotonin, 5-HT2A Receptors, Serotonin/*geneticsJul 8Recent evidence that 5HT-2A may be subjected to genomic imprinting prompted us to examine a collection of Irish family trios (an affected individual and both parents) for evidence of an association between 5HT-2A and bipolar disorder. Family trios offer an advantage over case control studies in regard to genomic imprinting since with family trios it is possible to trace the path of alleles from the parents to the offspring. Using haplotype-based haplotype relative risk (HHRR) and transmission/disequilibrium (TDT) analyses, no evidence was found for an association of 5HT-2A with bipolar affective disorder under the assumption of no imprinting and of imprinting.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11449393 B0148-7299 (Print) Journal Article Research Support, Non-U.S. Gov't11449393=Genetics Department, Trinity College Dublin, Dublin, Ireland.~?Murray, J. C. Buetow, K. H. Weber, J. L. Ludwigsen, S. Scherpbier-Heddema, T. Manion, F. Quillen, J. Sheffield, V. C. Sunden, S. Duyk, G. M. et al.,1994cA comprehensive human linkage map with centimorgan density. Cooperative Human Linkage Center (CHLC)2049-54Science2655181*Chromosome Mapping Chromosomes, Human Databases, Factual Female Genetic Markers *Genome, Human Genotype *Human Genome Project Humans MaleSep 30BIn the last few years there have been rapid advances in developing genetic maps for humans, greatly enhancing our ability to localize and identify genes for inherited disorders. Through the collaborative efforts of three large groups generating microsatellite markers and the efforts of the 110 CEPH collaborators, a comprehensive human linkage map is presented here. It consists of 5840 loci, of which 970 are uniquely ordered, covering 4000 centimorgans on the sex-averaged map. Of these loci, 3617 are polymerase chain reaction-formatted short tandem repeat polymorphisms, and another 427 are genes. The map has markers at an average density of 0.7 centimorgan, providing a resource for ready transference to physical maps and achieving one of the first goals of the Human Genome Project--a comprehensive, high-density genetic map.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8091227 !0036-8075 (Print) Journal Article8091227>Department of Pediatrics, University of Iowa, Iowa City 52245.?%Muthén, B Asparouhov, T. Rebollo, I.kAdvances in behavioral genetics modeling using Mplus: applications of factor mixture modeling to twin data.313-328Twin Res. Hum. Genet. 9r?Muthén, L. Muthén, B 1998-2002Mplus User’s GuideLos Angeles, CA.Muthén and Muthén?Muthén, L.K. Muthén, B.O2002LHow to use a Monte Carlo study to decide on sample size and determine power.599-620Struct. Equation Modeling9n? Myung, I.J.2003)Tutorial on maximum likelihood estimation90-100J. Math. Psychol.47V~?Nance, W. E. Neale, M. C.1989(Partitioned twin analysis: a power study143-50 Behav Genet191*Computer Simulation DNA/*genetics Humans *Models, Genetic *Polymorphism, Genetic *Polymorphism, Restriction Fragment Length *Software *TwinsJanhIndividual differences in the human genome may now be measured with molecular genetic techniques. Therefore, dizygotic (DZ) twins may be classified as sharing two, one, or zero "genes" identical by descent for any measured polymorphism. As a result, we may partition genetic variation into two sources: (i) genotypes at and closely linked to particular marker loci identified with restriction fragment length polymorphisms (RFLPs) and (ii) other genetic variation. The power of the classical twin study to reject false models lacking either a marker effect or a residual genetic effect is explored. Additivity of genetic effects at or near the locus and of the residual genetic variation as well as random environmental variation are assumed. Results indicate that statistical rejection of models could be achieved with sample sizes which are within the range of several current twin registers. A design including monozygotic (MZ) twins is compared with one consisting of only DZ twins. MZ twins add considerable power for the detection of residual genetic variation but provide no information to resolve genetic marker effects.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2565716 g0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.2565716~?dNash, M. W. Sugden, K. Huezo-Diaz, P. Williamson, R. Sterne, A. Purcell, S. Sham, P. C. Craig, I. W.2005zAssociation analysis of monoamine genes with measures of depression and anxiety in a selected community sample of siblings33-7%Am J Med Genet B Neuropsychiatr Genet1351Alleles Anxiety Disorders/*genetics Depressive Disorder/*genetics Female Gene Frequency Genetic Predisposition to Disease/*genetics Genotype Humans Male Microsatellite Repeats/genetics Monoamine Oxidase/genetics Phenotype Questionnaires Receptor, Serotonin, 5-HT1B/genetics Receptor, Serotonin, 5-HT1D/genetics Receptor, Serotonin, 5-HT2C/genetics Siblings Tryptophan Hydroxylase/geneticsMay 5Evidence indicates the genetic susceptibility to depression and anxiety is both overlapping and dimensional. In the current study, a quantitative phenotype had been created from several depression and anxiety-related measures in order to index this common genetic susceptibility (G). This has been studied in 119 sibships comprising 312 individuals, selected for extreme scores on G, from a community-based sample of 34,371 individuals. In a pathway based candidate gene study, we examined five microsatellite markers located within or nearby to five serotonin system genes (5HT2C, 5HT1D, 5HT1B, TPH1, and MAOB). Statistical analysis, carried out using QTDT, gave a significant association with a microsatellite downstream of TPH1. Further analysis included a life-events composite as a co-variable, this lead to a stronger association of TPH1. To our knowledge, this is the first study to report an association of the 3' end of TPH1 with continuous measures of depression and anxiety.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15729745 B1552-4841 (Print) Journal Article Research Support, Non-U.S. Gov't15729745Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, London, United Kingdom. m.nash@iop.kcl.ac.uk;~?Neale, B. M. Sham, P. C.2004FThe future of association studies: gene-based analysis and replication353-62Am J Hum Genet753Alleles Gene Frequency Genetic Predisposition to Disease Haplotypes Humans Linkage Disequilibrium Mass Screening Meta-Analysis *Models, Genetic Polymorphism, Single Nucleotide Statistics Variation (Genetics)SepiHistorically, association tests were limited to single variants, so that the allele was considered the basic unit for association testing. As marker density increases and indirect approaches are used to assess association through linkage disequilibrium, association is now frequently considered at the haplotypic level. We suggest that there are difficulties in replicating association findings at the single-nucleotide-polymorphism (SNP) or the haplotype level, and we propose a shift toward a gene-based approach in which all common variation within a candidate gene is considered jointly. Inconsistencies arising from population differences are more readily resolved by use of a gene-based approach rather than either a SNP-based or a haplotype-based approach. A gene-based approach captures all of the potential risk-conferring variations; thus, negative findings are subject only to the issue of power. In addition, chance findings due to multiple testing can be readily accounted for by use of a genewide-significance level. Meta-analysis procedures can be formalized for gene-based methods through the combination of P values. It is only a matter of time before all variation within genes is mapped, at which point the gene-based approach will become the natural end point for association analysis and will inform our search for functional variants relevant to disease etiology.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15272419 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15272419}Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, London, United Kingdom.? Neale, M.C.2000HFlexible QTL mapping with Mx. In: Advances in Twin and Sib-pair Analysis217-243&T.D. Spector H. Snieder A.J. MacGregorLondonGreenwich Medical Media ~? Neale, M. C.2003AA finite mixture distribution model for data collected from twins235-9Twin Res63@Humans *Models, Statistical Research Design *Twin Studies *TwinsJunMost analyses of data collected from a classical twin study of monozygotic (MZ) and dizygotic (DZ) twins assume that zygosity has been diagnosed without error. However, large scale surveys frequently resort to questionnaire-based methods of diagnosis which classify twins as MZ or DZ with less than perfect accuracy. This article describes a mixture distribution approach to the analysis of twin data when zygosity is not perfectly diagnosed. Estimates of diagnostic accuracy are used to weight the likelihood of the data according to the probability that any given pair is either MZ or DZ. The performance of this method is compared to fully accurate diagnosis, and to the analysis of samples that include some misclassified pairs. Conventional analysis of samples containing misclassified pairs yields biased estimates of variance components, such that additive genetic variance (A) is underestimated while common environment (C) and specific environment (E) components are overestimated. The bias is non-trivial; for 10% misclassification, true values of Additive genetic: Common environment: Specific Environment variance components of.6:.2:.2 are estimated as.48:.29:.23, respectively. The mixture distribution yields unbiased estimates, while showing relatively little loss of statistical precision for misclassification rates of 15% or less. The method is shown to perform quite well even when no information on zygosity is available, and may be applied when pair-specific estimates of zygosity probabilities are available.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12855073 F1369-0523 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.12855073Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond 23298, USA. neale@hsc.vcu.eduj?&Neale, M.C. Boker, S. Xie, G. Maes, HH2002Mx: Statistical Modeling.RichmondVCU?Neale, M.C Cardon, L.R1992sMethodology for Genetic Studies of Twins and Families (NATO ASI Series D: Behavioural and Social Sciences, Vol. 67) DordrechtKluwer ?XNeale, M.C. Cherny, S.S. Sham, P.C. Whitfield, J.B. Heath, A.C. Birley, A.J. Martin, N.G1999zDistinguishing population stratification from genuine allelic effects in Mx: association of ADH2 with alcohol consumption.233-243 Behav. Genet.29~?(Neale, M. C. Eaves, L. J. Kendler, K. S.1994NThe power of the classical twin study to resolve variation in threshold traits239-58 Behav Genet243Bias (Epidemiology) Humans Intelligence/genetics *Models, Genetic Models, Statistical Personality/genetics *Phenotype *Social Environment Twins, Dizygotic/genetics Twins, Monozygotic/genetics *Variation (Genetics)MayWe explore the power of the twin study to resolve sources of familial resemblance when the data are measured at the binary or ordinal level. Four components of variance were examined: additive genetic, nonadditive genetic, and common and specific environment. Curves are presented to compare the power of the continuous case with those of threshold models corresponding to different prevalences in the population: 1, 5, 10, 25, and 50%. Approximately three times the sample size is needed for equivalent power to the continuous case when the threshold is at the optimal 50%, and this ratio increases to about 10 times when 10% are above threshold. Some power may be recovered by subdividing those above threshold to form three or more ordered classes, but power is determined largely by the lowest threshold. Non-random ascertainment of twins (i) through affected twins and examining their cotwins or (ii) through ascertainment of all pairs in which at least one twin is affected increases power. In most cases, strategy i is more efficient than strategy ii. Though powerful for the rarer disorders, these methods suffer the disadvantage that they rely on prior knowledge of the population prevalence. Furthermore, sampling from hospital cases may introduce biases, reducing their value. A useful approach may be to assess the population with a screening instrument; the power calculations indicate that sampling all concordant and half of the discordant pairs would be efficient, as along as the cost of screening is not too high.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7945154 Q0001-8244 (Print) Journal Article Research Support, U.S. Gov't, P.H.S. Twin Study7945154KDepartment of Psychiatry, Medical College of Virginia, Richmond 23298-0710.d~?0Neale, M. C. Lubke, G. Aggen, S. H. Dolan, C. V.2005nProblems with using sum scores for estimating variance components: contamination and measurement noninvariance553-68Twin Res Hum Genet86Humans *Models, Genetic Multivariate Analysis *Quantitative Trait, Heritable Twins, Dizygotic/*genetics Twins, Monozygotic/*geneticsDecTwin studies of complex traits, such as behavior or psychiatric diagnoses, frequently involve univariate analysis of a sum score derived from multiple items. In this article, we show that absence of measurement invariance across zygosity can bias estimates of genetic and environmental components of variance. Specifically, if the item responses are considered as multiple indicators of a latent factor, and the aim is to partition the variance in the latent factor, then the factor loadings relating the items to the factor should be equal for monozygotic (MZ) and dizygotic (DZ) twins. While it seems unlikely, a priori, that these loadings should differ as a function of zygosity, certain special measurement situations are cause for concern. Ratings by parents, or self-ratings of phenotypes which are more easily observed in others than via introspection, may be tainted by the co-twin's phenotype to a greater extent in MZ than DZ pairs. We also show that the analysis of sum scores typically biases both MZ and DZ correlations compared to the true latent trait correlation. These two sources of bias are quantified for a range of values and are shown to be especially acute for sum scores based on binary items. Solutions to these problems include formal tests for measurement invariance across zygosity prior to analysis of the sum or scale scores, and multivariate genetic analysis at the individual item or symptom level.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16354497 Q1832-4274 (Print) Journal Article Research Support, N.I.H., Extramural Twin Study16354497Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, USA 23219-1534, USA. mcneale@vcu.edu|?Neale, M.C Maes, HHIn press6Methodology for Genetic Studies of Twins and Families DordrechtKluwer~?Neale, M. C. Miller, M. B.1997BThe use of likelihood-based confidence intervals in genetic models113-20 Behav Genet272Adult *Confidence Intervals Depressive Disorder/genetics/psychology Diseases in Twins/genetics Female Humans *Likelihood Functions *Models, Genetic Social Environment Twins, Dizygotic/genetics/psychology Twins, Monozygotic/genetics/psychologyMarGThis article describes the computation and relative merits of likelihood-based confidence intervals, compared to other measures of error in parameter estimates. Likelihood-based confidence intervals have the advantage of being asymmetric, which is often the case with structural equation models for genetically informative studies. We show how the package Mx provides confidence intervals for parameters and functions of parameters in the context of a simple additive genetic, common, and specific environment threshold model for binary data. Previously published contingency tables for major depression in adult female twins are used for illustration. The support for the model shows a marked skew as the additive genetic parameter is systematically varied from zero to one. The impact of allowing different prevalence rates in MZ vs. DZ twins is explored by fitting a model with separate threshold parameters and comparing the confidence intervals. Despite the improvement in fit of the different prevalence model, the confidence intervals on all parameters broaden, owing to their covariance.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9145549 Q0001-8244 (Print) Journal Article Research Support, U.S. Gov't, P.H.S. Twin Study9145549dDepartment of Psychiatry, Medical College of Virginia, Richmond 23298, USA. neale@psycho.psi.vcu.edu~?%Neale, M. C. Roysamb, E. Jacobson, K.2006EMultivariate genetic analysis of sex limitation and G x E interaction481-9Twin Res Hum Genet94JFemale Humans Male *Models, Genetic Sex Factors Twins, Dizygotic/*geneticsAugqSex-limited expression of genetic or environmental factors occurs in two basic forms. First, the effects of a factor may be larger on one sex than on another, which is known as scalar sex limitation. Second, some factors may have an effect on one sex but not on the other, which is called nonscalar sex limitation. In the classical twin study, scalar sex-limited effects cause same-sex male and same-sex female twin correlations to differ. Nonscalar sex-limited effects would cause the correlations between opposite-sex pairs of relatives to be lower than would be expected from the correlations between relatives of the same sex. One approach to modeling such effects is to allow the genetic correlation between opposite-sex dizygotic twins to be less than one-half; another is to allow the common environment correlation for opposite-sex pairs to be less than unity. Extension of this approach to the multivariate case is not straightforward. Direct extension of the Cholesky decomposition such that each Cholesky factor is allowed to correlate less than one-half in opposite-sex pairs yields a model where the order of the variables can change the goodness-of-fit of the model. It is shown that similar problems exist with a variety of multivariate and longitudinal models, and in a variety of models of genotype x environment interaction. Several solutions to these problems are described.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16899154 _1832-4274 (Print) Comparative Study Journal Article Research Support, N.I.H., Extramural Review16899154Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia 23298-0126, United States of America. neale@hsc.vcu.eduU~?%Nielsen, D. M. Ehm, M. G. Weir, B. S.1998cDetecting marker-disease association by testing for Hardy-Weinberg disequilibrium at a marker locus1531-40Am J Hum Genet635Alleles Genes, Recessive Genetic Diseases, Inborn/*genetics *Genetic Markers Genetic Predisposition to Disease Heterozygote Humans *Linkage Disequilibrium *Models, Genetic *Models, StatisticalNovWe review and extend a recent suggestion that fine-scale localization of a disease-susceptibility locus for a complex disease be done on the basis of deviations from Hardy-Weinberg equilibrium among affected individuals. This deviation is driven by linkage disequilibrium between disease and marker loci in the whole population and requires a heterogeneous genetic basis for the disease. A finding of marker-locus Hardy-Weinberg disequilibrium therefore implies disease heterogeneity and marker-disease linkage disequilibrium. Although a lack of departure of Hardy-Weinberg disequilibrium at marker loci implies that disease susceptibilityweighted linkage disequilibria are zero, given disease heterogeneity, it does not follow that the usual measures of linkage disequilibrium are zero. For disease-susceptibility loci with more than two alleles, therefore, care is needed in the drawing of inferences from marker Hardy-Weinberg disequilibria.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9867708 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9867708WBioinformatics Department, Glaxo Wellcome, Inc., Research Triangle Park, NC 27709, USA.W~?$Niu, T. Qin, Z. S. Xu, X. Liu, J. S.2002PBayesian haplotype inference for multiple linked single-nucleotide polymorphisms157-69Am J Hum Genet701Algorithms Bayes Theorem Chromosome Mapping/*methods Chromosomes, Human, Pair 5/genetics Computer Simulation Cystic Fibrosis Transmembrane Conductance Regulator/genetics Genetic Markers Haplotypes/*genetics Humans Likelihood Functions Linkage (Genetics)/*genetics Models, Genetic Monte Carlo Method Peptidyl-Dipeptidase A/genetics Polymorphism, Single Nucleotide/*genetics Receptors, Adrenergic, beta-2/genetics Recombination, Genetic/genetics Research Design Sensitivity and Specificity SoftwareJanqHaplotypes have gained increasing attention in the mapping of complex-disease genes, because of the abundance of single-nucleotide polymorphisms (SNPs) and the limited power of conventional single-locus analyses. It has been shown that haplotype-inference methods such as Clark's algorithm, the expectation-maximization algorithm, and a coalescence-based iterative-sampling algorithm are fairly effective and economical alternatives to molecular-haplotyping methods. To contend with some weaknesses of the existing algorithms, we propose a new Monte Carlo approach. In particular, we first partition the whole haplotype into smaller segments. Then, we use the Gibbs sampler both to construct the partial haplotypes of each segment and to assemble all the segments together. Our algorithm can accurately and rapidly infer haplotypes for a large number of linked SNPs. By using a wide variety of real and simulated data sets, we demonstrate the advantages of our Bayesian algorithm, and we show that it is robust to the violation of Hardy-Weinberg equilibrium, to the presence of missing data, and to occurrences of recombination hotspots.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11741196 o0002-9297 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.11741196RProgram for Population Genetics, Harvard School of Public Health, Boston, MA, USA.T~?#North, B. V. Curtis, D. Sham, P. C.2002KA note on the calculation of empirical P values from Monte Carlo procedures439-41Am J Hum Genet712*Monte Carlo MethodAugfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12111669 0002-9297 (Print) Letter12111669~? Nyholt, D. R.2000All LODs are not created equal282-8Am J Hum Genet672Alleles Chromosome Mapping/*methods/*statistics & numerical data Female Humans Likelihood Functions *Lod Score Male Models, Genetic Nuclear Family Pedigree SoftwareAugfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10884360 H0002-9297 (Print) Comment Editorial Research Support, U.S. Gov't, P.H.S.10884360'~? Nyholt, D. R.2002BGENEHUNTER: your 'one-stop shop' for statistical genetic analysis?2-7 Hum Hered531Chromosome Mapping/*methods Computational Biology/*statistics & numerical data Humans *Linkage (Genetics) Linkage Disequilibrium Pedigree *Software Design StatisticsThe past decade has brought a proliferation of statistical genetic (linkage) analysis techniques, incorporating new methodology and/or improvement of existing methodology in gene mapping, specifically targeted towards the localization of genes underlying complex disorders. Most of these techniques have been implemented in user-friendly programs and made freely available to the genetics community. Although certain packages may be more 'popular' than others, a common question asked by genetic researchers is 'which program is best for me?'. To help researchers answer this question, the following software review aims to summarize the main advantages and disadvantages of the popular GENEHUNTER package.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11901265 I0001-5652 (Print) Journal Article Research Support, Non-U.S. Gov't Review11901265uQueensland Institute of Medical Research, Post Office Royal Brisbane Hospital, Brisbane, Australia. daleN@qimr.edu.auL~?O'Connell, J. R.2000EZero-recombinant haplotyping: applications to fine mapping using SNPsS64-70Genet Epidemiol 19 Suppl 1*Algorithms Female Gene Frequency Genotype *Haplotypes Humans Likelihood Functions Male Pedigree *Polymorphism, Single Nucleotide Recombination, Genetic SoftwareAs the number of single nucleotide polymorphisms (SNPs) available for genetic analysis increases, researchers will be saturating smaller and smaller regions of the genome with these biallelic markers in an effort to fine map complex diseases. An important tool in this fine-mapping effort is haplotyping. Algorithms are presented that find all possible haplotype configurations of the pedigree data under the assumption that there are no recombinants between the markers. These configurations can be used to estimate the haplotype frequencies, and identify the most common haplotypes in the data. These algorithms have been implemented into a software program (ZAPLO), and were tested on a published data set.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11055372 g0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11055372jDepartment of Human Genetics, University of Pittsburgh, Pennsylvania 15261, USA. jeff@watson.hgen.pitt.edu>~?O'Connell, J. R. Weeks, D. E.1995qThe VITESSE algorithm for rapid exact multilocus linkage analysis via genotype set-recoding and fuzzy inheritance402-8 Nat Genet114*Algorithms Chromosome Mapping/*methods Female Genetic Markers *Genotype Humans Likelihood Functions Linkage (Genetics) Male Pedigree *SoftwareDecAs genetic marker maps have improved, multipoint linkage analysis has become a crucial part of all disease mapping studies. Paradoxically, multipoint lod scores become increasingly difficult to compute, particularly as the numbers of markers, marker alleles and untyped people increase. We have solved this problem by using a novel set-recording scheme to recode each person's genotype and 'fuzzy inheritance' to infer transmission probabilities. Our approach is implemented in a memory-efficient computer program, VITESSE, for extremely rapid computation of exact multipoint likelihoods. VITESSE enables fast and precise multipoint mapping of disease loci with highly polymorphic markers.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7493020 g1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.7493020PDepartment of Human Genetics, University of Pittsburgh, Pennsylvania 15261, USA.~?O'Connell, J. R. Weeks, D. E.1998XPedCheck: a program for identification of genotype incompatibilities in linkage analysis259-66Am J Hum Genet631*Algorithms Genetic Diseases, Inborn/diagnosis/genetics *Genotype Humans Linkage (Genetics)/*genetics Pedigree Research Design SoftwareJulWPrior to performance of linkage analysis, elimination of all Mendelian inconsistencies in the pedigree data is essential. Often, identification of erroneous genotypes by visual inspection can be very difficult and time consuming. In fact, sometimes the errors are not recognized until the stage of running linkage-analysis software. The effort then required to find the erroneous genotypes and to cross-reference pedigree and marker data that may have been recoded and renumbered can be not only tedious but also quite daunting, in the case of very large pedigrees. We have implemented four error-checking algorithms in a new computer program, PedCheck, which will assist researchers in identifying all Mendelian inconsistencies in pedigree data and will provide them with useful and detailed diagnostic information to help resolve the errors. Our program, which uses many of the algorithms implemented in VITESSE, handles large data sets quickly and efficiently, accepts a variety of input formats, and offers various error-checking algorithms that match the subtlety of the pedigree error. These algorithms range from simple parent-offspring-compatibility checks to a single-locus likelihood-based statistic that identifies and ranks the individuals most likely to be in error. We use various real data sets to illustrate the power and effectiveness of our program.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9634505 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.9634505nDepartment of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA. jeff@sherlock.hgen.pitt.edu~?/Odeberg, J. Holmberg, K. Eriksson, P. Uhlen, M.2002'Molecular haplotyping by pyrosequencing1104, 1106, 1108 Biotechniques335Alleles Amino Acid Substitution Cathepsins/genetics Codon/genetics Genotype Haplotypes/*genetics Humans Matrix Metalloproteinase 7/genetics Microspheres *Polymorphism, Single Nucleotide Sequence Analysis, DNA/*methods StreptavidinNovfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12449389 g0736-6205 (Print) Comparative Study Evaluation Studies Journal Article Research Support, Non-U.S. Gov't12449389hDepartment of Biotechnology, KTH, Royal Institute of Technology, Stockholm, Sweden. jacob@biotech.kth.se J~?Ogura, Y. Bonen, D. K. Inohara, N. Nicolae, D. L. Chen, F. F. Ramos, R. Britton, H. Moran, T. Karaliuskas, R. Duerr, R. H. Achkar, J. P. Brant, S. R. Bayless, T. M. Kirschner, B. S. Hanauer, S. B. Nunez, G. Cho, J. H.2001OA frameshift mutation in NOD2 associated with susceptibility to Crohn's disease603-6Nature4116837Adult Alleles Amino Acid Sequence Base Sequence *Carrier Proteins Case-Control Studies Cell Line Child Crohn Disease/*genetics Cytosine Dna Female *Frameshift Mutation Gene Frequency Genetic Predisposition to Disease Heterozygote Homozygote Humans *Intracellular Signaling Peptides and Proteins Lipopolysaccharides/pharmacology Male Molecular Sequence Data Mutagenesis, Insertional NF-kappa B/metabolism Nod2 Signaling Adaptor Protein Polymerase Chain Reaction Protein Structure, Tertiary Proteins/*geneticsMay 31~Crohn's disease is a chronic inflammatory disorder of the gastrointestinal tract, which is thought to result from the effect of environmental factors in a genetically predisposed host. A gene location in the pericentromeric region of chromosome 16, IBD1, that contributes to susceptibility to Crohn's disease has been established through multiple linkage studies, but the specific gene(s) has not been identified. NOD2, a gene that encodes a protein with homology to plant disease resistance gene products is located in the peak region of linkage on chromosome 16 (ref. 7). Here we show, by using the transmission disequilibium test and case-control analysis, that a frameshift mutation caused by a cytosine insertion, 3020insC, which is expected to encode a truncated NOD2 protein, is associated with Crohn's disease. Wild-type NOD2 activates nuclear factor NF-kappaB, making it responsive to bacterial lipopolysaccharides; however, this induction was deficient in mutant NOD2. These results implicate NOD2 in susceptibility to Crohn's disease, and suggest a link between an innate immune response to bacterial components and development of disease.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11385577 g0028-0836 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11385577Department of Pathology and Comprehensive Cancer Center, The University of Michigan Medical School, Ann Arbor, Michigan 48109, USA.~? Olson, J. M.1999LRelationship estimation by Markov-process models in a sib-pair linkage study1464-72Am J Hum Genet645C*Alleles *Chromosome Mapping Humans *Markov Chains *Models, GeneticMayiThe results of sib-pair linkage studies may be compromised if a substantial number of putative sib pairs are not actually sib pairs. For classification of pairs in a sib-pair genome scan, I propose multipoint methods that are based on a Markov-process model of allele sharing along the chromosome. These methods can be implemented by standard algorithms that compute multipoint marker allele-sharing probabilities for sib pairs. When marker data from at least half the genome are used, misclassification rates are small. The methods will be implemented in an upcoming version of the computer software package S.A.G.E.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10205280 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10205280Department of Epidemiology and Biostatistics, Case Western Reserve University, MetroHealth Medical Center R-255, Cleveland, OH 44109, USA. olson@darwin.cwru.eduD?Open Source Initiative,2005http://opensource.orgX~?Ott, J.1976BA computer program for linkage analysis of general human pedigrees528-9Am J Hum Genet285A*Computers *Genetics, Medical Humans *Linkage (Genetics) PedigreeSepdhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=984049 0002-9297 (Print) Letter984049t?Ott, J.1991!Analysis of human genetic linkageBaltimore, MD.Johns Hopkins University PressV~?Ott, J.19936Detecting marker inconsistencies in human gene mapping25-30 Hum Hered431T*Chromosome Mapping *Genetic Markers Genotype Humans Pedigree Phenotype Risk FactorsJan-FebWhen an inconsistency occurs in a pedigree, it may not be apparent which individual(s) are causing it. Here, a statistical method is described which identifies individuals most likely to have caused an inconsistency. The method is based on the sum of squared deviations between two predictors of an individual's genotypes: (1) that given an individual's own phenotype, and (2) that given all phenotypes in the pedigree. Extreme deviations between the two arrays (measured in terms of a sum of squares) are interpreted as indicating an inconsistency. The method is applied to a pedigree with an inconsistency in which it is unclear who is causing the inconsistency.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8514322 F0001-5652 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8514322*Columbia University, New York, N.Y. 10032.~?POwerbach, D. Naya, F. J. Tsai, M. J. Allander, S. V. Powell, D. R. Gabbay, K. H.1997Analysis of candidate genes for susceptibility to type I diabetes: a case-control and family-association study of genes on chromosome 2q31-351069-74Diabetes466Alleles Antigens, CD28/genetics Basic Helix-Loop-Helix Transcription Factors Case-Control Studies Chromosome Mapping Chromosomes, Human, Pair 2/*genetics DNA Primers/chemistry DNA-Binding Proteins/*genetics Diabetes Mellitus, Type 1/*genetics/immunology Family Gene Frequency HLA-DR Antigens/immunology Homeodomain Proteins/*genetics Humans Insulin-Like Growth Factor Binding Protein 2/genetics Insulin-Like Growth Factor Binding Protein 5/*genetics Linkage (Genetics) Microsatellite Repeats/genetics Multigene Family Polymerase Chain Reaction Polymorphism, Single-Stranded Conformational Trans-Activators/*genetics Transcription Factors/*geneticsJunRecent genome searches suggest a putative linkage of many loci to susceptibility to type I diabetes. The chromosome 2q31-35 region is reported to be linked to susceptibility to type I diabetes and is thought to contain several diabetes susceptibility loci. These candidate genes include the HOXD gene cluster, BETA2, CTLA4, CD28, IGFBP2, and IGFBP5. Association studies in populations and families are required to confirm and/or identify the actual susceptibility loci. We hereby report several previously unknown DNA polymorphisms for HOXD8, BETA2, and IGFBP5, which we have used along with previously known polymorphisms of HOXD8 and CTLA4 to test whether these candidate loci are the susceptibility genes on chromosome 2q31-35. Using a case-control design with a subsequent family-association approach to confirm associations, we find no evidence that these candidate genes are associated with susceptibility to type I diabetes.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9166681 y0012-1797 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.9166681hDepartment of Pediatrics, Baylor College of Medicine, Houston, Texas 77030, USA. davido@mbcr.bcm.tmc.edu L~?sOzaki, K. Ohnishi, Y. Iida, A. Sekine, A. Yamada, R. Tsunoda, T. Sato, H. Sato, H. Hori, M. Nakamura, Y. Tanaka, T.2002nFunctional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction650-4 Nat Genet324NAged Amino Acid Substitution Case-Control Studies Cells, Cultured Chromosome Mapping Chromosomes, Human, Pair 6 Coronary Vessels/metabolism Databases, Genetic Gene Frequency *Genetic Predisposition to Disease Genetic Screening Genotype Haplotypes Homozygote Humans Introns Jurkat Cells Linkage Disequilibrium Lymphotoxin-alpha/*genetics Middle Aged Muscle, Smooth, Vascular/cytology/physiology Myocardial Infarction/*genetics *Polymorphism, Single Nucleotide Recombinant Fusion Proteins/metabolism Sequence Analysis, DNA Transcription, Genetic Vascular Cell Adhesion Molecule-1/biosynthesisDectBy means of a large-scale, case-control association study using 92,788 gene-based single-nucleotide polymorphism (SNP) markers, we identified a candidate locus on chromosome 6p21 associated with susceptibility to myocardial infarction. Subsequent linkage-disequilibrium (LD) mapping and analyses of haplotype structure showed significant associations between myocardial infarction and a single 50 kb halpotype comprised of five SNPs in LTA (encoding lymphotoxin-alpha), NFKBIL1 (encoding nuclear factor of kappa light polypeptide gene enhancer in B cells, inhibitor-like 1) and BAT1 (encoding HLA-B associated transcript 1). Homozygosity with respect to each of the two SNPs in LTA was significantly associated with increased risk for myocardial infarction (odds ratio = 1.78, chi(2) = 21.6, P = 0.00000033; 1,133 affected individuals versus 1,006 controls). In vitro functional analyses indicated that one SNP in the coding region of LTA, which changed an amino-acid residue from threonine to asparagine (Thr26Asn), effected a twofold increase in induction of several cell-adhesion molecules, including VCAM1, in vascular smooth-muscle cells of human coronary artery. Moreover, the SNP, in intron 1 of LTA, enhanced the transcriptional level of LTA. These results indicate that variants in the LTA are risk factors for myocardial infraction and implicate LTA in the pathogenesis of the disorder.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12426569 B1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't12426569Laboratory for Cardiovascular Diseases, SNP Research Center, The Institute of Physical and Chemical Research (RIKEN), 4-6-1, Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.~? Palmer, L. J.2003|Loosening the cuff: important new advances in modeling antihypertensive treatment effects in genetic studies of hypertension197-8 Hypertension412Blood Pressure/*genetics/physiology Genetic Predisposition to Disease/genetics Humans Hypertension/*genetics/physiopathology/prevention & controlFebfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12574080 (1524-4563 (Electronic) Comment Editorial12574080*~? Palmer, L. J. Cardon, L. R.2005KShaking the tree: mapping complex disease genes with linkage disequilibrium1223-34Lancet3669492*Chromosome Mapping Gene Frequency Genetics, Population Genotype Haplotypes Humans *Linkage Disequilibrium Polymorphism, Single NucleotideOct 17Much effort and expense are being spent internationally to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, the technology for detecting and genotyping single nucleotide polymorphisms (SNPs) has undergone rapid development, yielding extensive catalogues of these polymorphisms across the genome. Population-based maps of the correlations amongst SNPs (linkage disequilibrium) are now being developed to accelerate the discovery of genes for complex human diseases. These genomic advances coincide with an increasing recognition of the importance of very large sample sizes for studying genetic effects. Together, these new genetic and epidemiological data hold renewed promise for the identification of susceptibility genes for complex traits. We review the state of knowledge about the structure of the human genome as related to SNPs and linkage disequilibrium, discuss the potential applications of this knowledge to mapping complex disease genes, and consider the issues facing whole genome association scanning using SNPs.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16198771 1474-547X (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.16198771Western Australian Institute for Medical Research and University of Western Australia Centre for Medical Research, University of Western Australia. lyle.palmer@cyllene.uwa.edu.au~? )Palmer, L. J. Jacobs, K. B. Elston, R. C.2000xHaseman and Elston revisited: the effects of ascertainment and residual familial correlations on power to detect linkage456-60Genet Epidemiol194k*Family Health Humans *Linkage (Genetics) Models, Genetic Models, Statistical Quantitative Trait, HeritableDecfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11108653 0741-0395 (Print) Comment Letter11108653 ~? Parra, E. J. Marcini, A. Akey, J. Martinson, J. Batzer, M. A. Cooper, R. Forrester, T. Allison, D. B. Deka, R. Ferrell, R. E. Shriver, M. D.1998WEstimating African American admixture proportions by use of population-specific alleles1839-51Am J Hum Genet636sAfrica/ethnology African Americans African Continental Ancestry Group/classification/*genetics *Alleles Alu Elements/genetics DNA, Mitochondrial/genetics Europe/ethnology Female Gene Frequency Gene Pool Genetic Markers *Genetics, Population Haplotypes/genetics Humans Jamaica Linkage Disequilibrium Male Polymorphism, Genetic Sex Ratio United States Y Chromosome/geneticsDecWe analyzed the European genetic contribution to 10 populations of African descent in the United States (Maywood, Illinois; Detroit; New York; Philadelphia; Pittsburgh; Baltimore; Charleston, South Carolina; New Orleans; and Houston) and in Jamaica, using nine autosomal DNA markers. These markers either are population-specific or show frequency differences >45% between the parental populations and are thus especially informative for admixture. European genetic ancestry ranged from 6.8% (Jamaica) to 22.5% (New Orleans). The unique utility of these markers is reflected in the low variance associated with these admixture estimates (SEM 1.3%-2.7%). We also estimated the male and female European contribution to African Americans, on the basis of informative mtDNA (haplogroups H and L) and Y Alu polymorphic markers. Results indicate a sex-biased gene flow from Europeans, the male contribution being substantially greater than the female contribution. mtDNA haplogroups analysis shows no evidence of a significant maternal Amerindian contribution to any of the 10 populations. We detected significant nonrandom association between two markers located 22 cM apart (FY-null and AT3), most likely due to admixture linkage disequilibrium created in the interbreeding of the two parental populations. The strength of this association and the substantial genetic distance between FY and AT3 emphasize the importance of admixed populations as a useful resource for mapping traits with different prevalence in two parental populations.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9837836 o0002-9297 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.9837836eDepartment of Human Genetics, Allegheny University of Health Sciences, Pittsburgh, Pennsylvania, USA.~? 'Patil, N. Berno, A. J. Hinds, D. A. Barrett, W. A. Doshi, J. M. Hacker, C. R. Kautzer, C. R. Lee, D. H. Marjoribanks, C. McDonough, D. P. Nguyen, B. T. Norris, M. C. Sheehan, J. B. Shen, N. Stern, D. Stokowski, R. P. Thomas, D. J. Trulson, M. O. Vyas, K. R. Frazer, K. A. Fodor, S. P. Cox, D. R.2001aBlocks of limited haplotype diversity revealed by high-resolution scanning of human chromosome 211719-23Science2945547Algorithms Alleles Animals Chromosomes, Human, Pair 21/*genetics Continental Population Groups/genetics Ethnic Groups/genetics Gene Frequency/genetics Genome, Human Haplotypes/*genetics Humans Hybrid Cells/metabolism Mutation/genetics Oligonucleotide Array Sequence Analysis/*methods Polymorphism, Single Nucleotide/*genetics Random Allocation Sensitivity and Specificity Variation (Genetics)/geneticsNov 23]Global patterns of human DNA sequence variation (haplotypes) defined by common single nucleotide polymorphisms (SNPs) have important implications for identifying disease associations and human traits. We have used high-density oligonucleotide arrays, in combination with somatic cell genetics, to identify a large fraction of all common human chromosome 21 SNPs and to directly observe the haplotype structure defined by these SNPs. This structure reveals blocks of limited haplotype diversity in which more than 80% of a global human sample can typically be characterized by only three common haplotypes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11721056 !0036-8075 (Print) Journal Article11721056KPerlegen Sciences, Inc., 2021 Stierlin Court, Mountain View, CA 94043, USA. L~? APeacock, M. Koller, D. L. Lai, D. Hui, S. Foroud, T. Econs, M. J.2005rSex-specific quantitative trait loci contribute to normal variation in bone structure at the proximal femur in men467-73Bone374Adult Chromosomes, Human, X Female Femur/*anatomy & histology Humans Lod Score Male Premenopause *Quantitative Trait Loci *Sex Factors *Variation (Genetics)Oct9Bone structure is an important determinant of osteoporotic fracture. In women, bone structure is highly heritable, and several quantitative trait loci (QTL) have been reported. There are few comparable data in men. This study in men aimed at establishing the heritability of bone structure at the proximal femur, identifying QTL contributing to normal variation in bone structure, and determining which QTL might be sex-specific. Bone structure at the proximal femur was measured in 205 pairs of brothers age 18-61. Heritability was calculated, and linkage analysis performed on phenotypes at the proximal femur. Heritability estimates ranged from 0.99 to 0.39. A genome wide scan identified suggestive QTL (LOD>2.2) for femoral shaft width on chromosome 14q (LOD=2.69 at position 99 cM), calcar femorale at chromosome 2p (LOD=3.97 at position 194 cM) and at the X chromosome (LOD=3.01 at position 77 cM), femoral neck width on chromosome 5p (LOD=2.28 at position 0 cM), femoral head width on chromosome 11q (LOD=2.30 at position 131 cM) and 15q (LOD=3.11 at position 90 cM), and pelvic axis length on chromosome 4q (LOD=4.16 at position 99 cM) and 17q (LOD=2.80 at position 112 cM). Comparison with published data in 437 pairs of premenopausal sisters from the same geographical region suggested that 3 of the 7 autosomal QTL were male-specific. This study demonstrates that bone structure at the proximal femur in healthy men is highly heritable. The occurrence of sex-specific genes in humans for bone structure has important implications for the pathogenesis and treatment of osteoporosis.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16046210 F8756-3282 (Print) Journal Article Research Support, N.I.H., Extramural16046210Department of Medicine, Indiana University School of Medicine, University Hospital and Out Patient Center, 550 N. University Boulevard, Room 5595, Indianapolis, IN 46202-5250, USA. mpeacock@iupui.edu?Pearson, K. Lee, A1901TOn the inheritance of characteristics not capable of exact quantitative measurement.79-150#Philos. Trans. R. Soc. Lond. Ser. A195~?"Peltonen, L. Palotie, A. Lange, K.20005Use of population isolates for mapping complex traits182-90 Nat Rev Genet13<Animals *Genetics, Population *Quantitative Trait, HeritableDecGeneticists have repeatedly turned to population isolates for mapping and cloning Mendelian disease genes. Population isolates possess many advantages in this regard. Foremost among these is the tendency for affected individuals to share ancestral haplotypes derived from a handful of founders. These haplotype signatures have guided scientists in the fine mapping of scores of rare disease genes. The past successes with Mendelian disorders using population isolates have prompted unprecedented interest among medical researchers in both the public and private sectors. Despite the obvious genetic and environmental complications, geneticists have targeted several population isolates for mapping genes for complex diseases.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11252747 n1471-0056 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Review11252747Department of Medical Genetics, University of Helsinki and National Public Health Institute, Finland. Lpeltonen@mednet.ucla.edu? Penrose, L1935rThe detection of autosomal linkage in data which consist of pairs of brothers and sisters of unspecified parentage133-138 Ann. Eugen.6j? Penrose, L1953*The general purpose sib-pair linkage test.120-124 Ann. Eugen. 18~?gPosthuma, D. Beem, A. L. de Geus, E. J. van Baal, G. C. von Hjelmborg, J. B. Iachine, I. Boomsma, D. I.2003,Theory and practice in quantitative genetics361-76Twin Res65rHumans Linkage (Genetics) Mathematics *Models, Theoretical Multivariate Analysis Twin Studies Variation (Genetics)Oct4With the rapid advances in molecular biology, the near completion of the human genome, the development of appropriate statistical genetic methods and the availability of the necessary computing power, the identification of quantitative trait loci has now become a realistic prospect for quantitative geneticists. We briefly describe the theoretical biometrical foundations underlying quantitative genetics. These theoretical underpinnings are translated into mathematical equations that allow the assessment of the contribution of observed (using DNA samples) and unobserved (using known genetic relationships) genetic variation to population variance in quantitative traits. Several statistical models for quantitative genetic analyses are described, such as models for the classical twin design, multivariate and longitudinal genetic analyses, extended twin analyses, and linkage and association analyses. For each, we show how the theoretical biometrical model can be translated into algebraic equations that may be used to generate scripts for statistical genetic software packages, such as Mx, Lisrel, SOLAR, or MERLIN. For using the former program a web-library (available from http://www.psy.vu.nl/mxbib) has been developed of freely available scripts that can be used to conduct all genetic analyses described in this paper.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14624720 I1369-0523 (Print) Journal Article Research Support, Non-U.S. Gov't Review14624720fDepartment of Biological Psychology, Vrije Universiteit Amsterdam, The Netherlands. danielle@psy.vu.nl ~?Posthuma, D. Boomsma, D. I.20008A note on the statistical power in extended twin designs147-58 Behav Genet302Analysis of Variance Humans Mathematical Computing Models, Genetic Phenotype *Quantitative Trait, Heritable *Social Environment Twin Studies/*statistics & numerical data Twins, Dizygotic/genetics Twins, Monozygotic/genetics *Variation (Genetics)MarThe power to detect sources of genetic and environmental variance varies with sample size, study design, effect size and the statistical significance level chosen. We explored whether the power of the classical twin study may be increased by adding non-twin siblings to the classical twin design. Sample sizes to detect genetic and shared environmental variation were compared for kinships with only twins, kinships consisting of twins and one additional sibling, and kinships with twins and two additional siblings. The effect of adding siblings to the classical twin design was considered for univariate and bivariate analyses. For the univariate case, adding one non-twin sibling resulted in a decrease in sample size needed to detect additive genetic influences in the presence of environmental influences. However, adding two additional siblings did not decrease the number of subjects as compared to the classical twin design. The sample size required to detect common environmental factors was also greatly decreased by adding one non-twin sibling. Adding two non-twin siblings resulted in a small additional decrease. In models including additive genetic, dominant genetic, and unique environmental effects, adding one sibling to a twin family decreased the required sample size to detect dominant genetic influences. Adding two siblings to a twin family resulted in only a slight additional decrease in sample size. In the bivariate case a similar pattern of results was found, in addition to the observation that the overall required sample size, as expected, was lower than in the univariate case. The decrease in sample size from bivariate testing was more pronounced in a design with one or two additional siblings, as compared to a design with twins only. It is concluded that a well considered choice of family design, i.e. including families with twins and one or two additional siblings increases the statistical power to detect sources of variance due to additive and non-additive genetic influences, and common environment.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10979605 B0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't10979605bDepartment of Biological Psychology, Vrije Universiteit Amsterdam, Netherlands. danielle@psy.vu.nl~?*Posthuma, D. de Geus, E. J. Boomsma, D. I.2001EPerceptual speed and IQ are associated through common genetic factors593-602 Behav Genet316Adult Cohort Studies Female Humans Intelligence/*genetics Male Middle Aged Netherlands Reaction Time/*genetics Social Environment Twins/*geneticsNovcIndividual differences in inspection time explain about 20% of IQ test variance. To determine whether the association between inspection time and IQ is mediated by common genes or by a common environmental factor, inspection time and IQ were assessed in an extended twin design. Data from 688 participants from 271 families were collected as part of a large ongoing project on the genetics of adult brain function and cognition. The sample consisted of a young adult cohort (mean age 26.2 years) and an older adult cohort (mean age 50.4 years). IQ was assessed with the Dutch version of the WAIS-3R. Inspection time was measured in the so-called II-paradigm, in which a subject is asked to decide which leg of the II-figure is longest at varying display times of the II-figure. The number of correct inspections per second (i.e., the reciprocal of inspection time) was used to index perceptual speed. For Verbal IQ and Performance IQ, heritabilities were 85% and 69%, respectively. For perceptual speed, 46% of the total variance was explained by genetic variance. No differences in heritability estimates across age cohorts or sexes were found. Across the whole sample, a significant phenotypic correlation was found between perceptual speed and Verbal IQ (0.19) and between perceptual speed and Performance IQ (0.27). These correlations were entirely due to a common genetic factor that accounted for 10% of the genetic variance in verbal IQ and for 22% of the genetic variance in performance IQ. This factor is hypothesized to reflect the influence of genetic factors that determine axonal myelination in the central nervous system.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11838536 M0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't Twin Study11838536gDepartment of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands. danielle@psy.vu.nl~?7Posthuma, D. de Geus, E. J. Boomsma, D. I. Neale, M. C.2004,Combined linkage and association tests in mx179-96 Behav Genet342Alleles Chromosome Mapping/*statistics & numerical data Gene Frequency/genetics Genes, Dominant/genetics Genetics, Population *Genotype Humans Mathematical Computing *Models, Genetic *Models, Statistical Quantitative Trait Loci/*genetics SoftwareMarStatistical methods aimed at the detection of genes for quantitative traits suffer from two problems: (i) when a linkage approach is employed, relatively large sample sizes are usually required; and (ii) when an association approach is employed, effects of population stratification may blur genuine locus-trait associations. The variance components method proposed by Fulker et al. (1999) addressed both these problems; it is statistically powerful because it involves a combined analysis of linkage and association and can include information from multiplex families, which reduces the overall amount of necessary individual genotypes. In addition, it includes an explicit test for the presence of spurious association. After a brief illustration of the various ways in which population stratification may affect locus-trait associations, the implementation in Mx (Neale, 1997) of the method as proposed by Fulker et al. (1999) is discussed and illustrated. In addition, an extension to this method is proposed that allows the use of (variable) sibship sizes greater than two, the estimation of additive and dominance association effects, and the use of multiple alleles. These extensions can be implemented when parental genotypes are available or unavailable.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14755183 B0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't14755183Department of Biological Psychology, Vrije Universiteit Amsterdam, van der Boechorststraat 1, 1081 BT The Netherlands. danielle@psy.vu.nl~?qPosthuma, D. Luciano, M. Geus, E. J. Wright, M. J. Slagboom, P. E. Montgomery, G. W. Boomsma, D. I. Martin, N. G.2005RA genomewide scan for intelligence identifies quantitative trait loci on 2q and 6p318-26Am J Hum Genet772&Adolescent Adult Autistic Disorder/genetics Chromosome Mapping *Chromosomes, Human, Pair 2 *Chromosomes, Human, Pair 6 Dyslexia/genetics Female Genome *Genome, Human Humans *Intelligence Learning Disorders/genetics Linkage (Genetics) Lod Score Male Middle Aged Mutation *Quantitative Trait LociAugSBetween 40% and 80% of the variation in human intelligence (IQ) is attributable to genetic factors. Except for many rare mutations resulting in severe cognitive dysfunction, attempts to identify these factors have not been successful. We report a genomewide linkage scan involving 634 sibling pairs designed to identify chromosomal regions that explain variation in IQ. Model-free multipoint linkage analysis revealed evidence of a significant quantitative-trait locus for performance IQ at 2q24.1-31.1 (LOD score 4.42), which overlaps the 2q21-33 region that has repeatedly shown linkage to autism. A second region revealed suggestive linkage for both full-scale and verbal IQs on 6p25.3-22.3 (LOD score 3.20 for full-scale IQ and 2.33 for verbal IQ), overlapping marginally with the 6p22.3-21.31 region implicated in reading disability and dyslexia.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16001363 0002-9297 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.16001363gDepartment of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands. danielle@psy.vu.nl~?.Presson, A. P. Sobel, E. Lange, K. Papp, J. C.2006Merging microsatellite data1131-47 J Comput Biol136Algorithms Alleles Bayes Theorem Gene Frequency *Genetic Markers Genotype Markov Chains *Microsatellite Repeats *Models, Genetic *Models, Statistical Monte Carlo Method *SoftwareJul-AugGenotype calling procedures vary from laboratory to laboratory for many microsatellite markers. Even within the same laboratory, application of different experimental protocols often leads to ambiguities. The impact of these ambiguities ranges from irksome to devastating. Resolving the ambiguities can increase effective sample size and preserve evidence in favor of disease-marker associations. Because different data sets may contain different numbers of alleles, merging is unfortunately not a simple process of matching alleles one to one. Merging data sets manually is difficult, time-consuming, and error-prone due to differences in genotyping hardware, binning methods, molecular weight standards, and curve fitting algorithms. Merging is particularly difficult if few or no samples occur in common, or if samples are drawn from ethnic groups with widely varying allele frequencies. It is dangerous to align alleles simply by adding a constant number of base pairs to the alleles of one of the data sets. To address these issues, we have developed a Bayesian model and a Markov chain Monte Carlo (MCMC) algorithm for sampling the posterior distribution under the model. Our computer program, MicroMerge, implements the algorithm and almost always accurately and efficiently finds the most likely correct alignment. Common allele frequencies across laboratories in the same ethnic group are the single most important cue in the model. MicroMerge computes the allelic alignments with the greatest posterior probabilities under several merging options. It also reports when data sets cannot be confidently merged. These features are emphasized in our analysis of simulated and real data.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16901233 F1066-5277 (Print) Journal Article Research Support, N.I.H., Extramural16901233\Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095, USA.~?UPrice, A. L. Patterson, N. J. Plenge, R. M. Weinblatt, M. E. Shadick, N. A. Reich, D.2006\Principal components analysis corrects for stratification in genome-wide association studies904-9 Nat Genet388Algorithms Alleles Case-Control Studies Databases, Nucleic Acid Genetic Markers Genome, Human Genomics/*statistics & numerical data Genotype Humans Phenotype Polymorphism, Single Nucleotide Principal Component AnalysisAugPopulation stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16862161 B1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't16862161fDepartment of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA. aprice@broad.mit.edu~?Pritchard, J. K. Donnelly, P.2001HCase-control studies of association in structured or admixed populations227-37Theor Popul Biol603Alleles *Case-Control Studies Chi-Square Distribution *Genetic Markers Genetics, Population Genotype Humans *Linkage (Genetics) *Models, Genetic Research Design SoftwareNov>Case-control tests for association are an important tool for mapping complex-trait genes. But population structure can invalidate this approach, leading to apparent associations at markers that are unlinked to disease loci. Family-based tests of association can avoid this problem, but such studies are often more expensive and in some cases--particularly for late-onset diseases--are impractical. In this review article we describe a series of approaches published over the past 2 years which use multilocus genotype data to enable valid case-control tests of association, even in the presence of population structure. These tests can be classified into two categories. "Genomic control" methods use the independent marker loci to adjust the distribution of a standard test statistic, while "structured association" methods infer the details of population structure en route to testing for association. We discuss the statistical issues involved in the different approaches and present results from simulations comparing the relative performance of the methods under a range of models.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11855957 I0040-5809 (Print) Journal Article Research Support, Non-U.S. Gov't Review11855957PDepartment of Statistics, University of Oxford, Oxford, OX1 3TG, United Kingdom.~?Pritchard, J. K. Przeworski, M.20011Linkage disequilibrium in humans: models and data1-14Am J Hum Genet691Chromosome Mapping/methods/statistics & numerical data *Genetics, Population Genome, Human Humans Linkage Disequilibrium/*genetics *Models, Genetic Polymorphism, Genetic/genetics Recombination, Genetic/genetics Selection (Genetics)Jul%In this review, we describe recent empirical and theoretical work on the extent of linkage disequilibrium (LD) in the human genome, comparing the predictions of simple population-genetic models to available data. Several studies report significant LD over distances longer than those predicted by standard models, whereas some data from short, intergenic regions show less LD than would be expected. The apparent discrepancies between theory and data present a challenge-both to modelers and to human geneticists-to identify which important features are missing from our understanding of the biological processes that give rise to LD. Salient features may include demographic complications such as recent admixture, as well as genetic factors such as local variation in recombination rates, gene conversion, and the potential segregation of inversions. We also outline some implications that the emerging patterns of LD have for association-mapping strategies. In particular, we discuss what marker densities might be necessary for genomewide association scans.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11410837 r0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Review11410837_Department of Statistics, University of Oxford, Oxford, OX1-3TG, England. pritch@stats.ox.ac.uk}~?!Pritchard, J. K. Rosenberg, N. A.1999ZUse of unlinked genetic markers to detect population stratification in association studies220-8Am J Hum Genet651sAlleles *Case-Control Studies *Genetic Markers Genotype Humans *Linkage (Genetics) *Models, Genetic Research DesignJulWe examine the issue of population stratification in association-mapping studies. In case-control studies of association, population subdivision or recent admixture of populations can lead to spurious associations between a phenotype and unlinked candidate loci. Using a model of sampling from a structured population, we show that if population stratification exists, it can be detected by use of unlinked marker loci. We show that the case-control-study design, using unrelated control individuals, is a valid approach for association mapping, provided that marker loci unlinked to the candidate locus are included in the study, to test for stratification. We suggest guidelines as to the number of unlinked marker loci to use.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10364535 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.10364535Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1-3TG United Kingdom. pritch@stats.ox.ac.uk pritch@stats.ox.ac.uk~?*Pritchard, J. K. Stephens, M. Donnelly, P.2000@Inference of population structure using multilocus genotype data945-59Genetics1552QAlgorithms Cluster Analysis *Genetics, Population Genotype Humans Models, GeneticJunqWe describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci-e.g. , seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/ approximately pritch/home. html.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10835412 g0016-6731 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.10835412TDepartment of Statistics, University of Oxford, United Kingdom. pritch@tats.ox.ac.uk~?;Pritchard, J. K. Stephens, M. Rosenberg, N. A. Donnelly, P.2000-Association mapping in structured populations170-81Am J Hum Genet671Alleles Case-Control Studies Chromosome Mapping/*methods/statistics & numerical data Computer Simulation Female Genetic Diseases, Inborn/genetics Genetic Markers/genetics *Genetics, Population Humans Linkage Disequilibrium/genetics Male Models, Genetic Nuclear Family Pedigree Phenotype Polymorphism, Single Nucleotide/genetics Reproducibility of Results Sensitivity and Specificity Statistical DistributionsJulThe use, in association studies, of the forthcoming dense genomewide collection of single-nucleotide polymorphisms (SNPs) has been heralded as a potential breakthrough in the study of the genetic basis of common complex disorders. A serious problem with association mapping is that population structure can lead to spurious associations between a candidate marker and a phenotype. One common solution has been to abandon case-control studies in favor of family-based tests of association, such as the transmission/disequilibrium test (TDT), but this comes at a considerable cost in the need to collect DNA from close relatives of affected individuals. In this article we describe a novel, statistically valid, method for case-control association studies in structured populations. Our method uses a set of unlinked genetic markers to infer details of population structure, and to estimate the ancestry of sampled individuals, before using this information to test for associations within subpopulations. It provides power comparable with the TDT in many settings and may substantially outperform it if there are conflicting associations in different subpopulations.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10827107 k0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.10827107UDepartment of Statistics, University of Oxford, United Kingdom. pritch@stats.ox.ac.uk~? Purcell, S.2002LVariance components models for gene-environment interaction in twin analysis554-71Twin Res56]Computer Simulation *Environment Genetics, Behavioral Humans *Models, Genetic Twins/*geneticsDec]Gene-environment interaction is likely to be a common and important source of variation for complex behavioral traits. Often conceptualized as the genetic control of sensitivity to the environment, it can be incorporated in variance components twin analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. The model allows for one or more environmental moderator variables (that possibly interact with each other) that may i). be continuous or binary ii). differ between twins within a pair iii). interact with residual environmental as well as genetic effects iv) have nonlinear moderating properties v). show scalar (different magnitudes) or qualitative (different genes) interactions vi). be correlated with genetic effects acting upon the trait, to allow for a test of gene-environment interaction in the presence of gene-environment correlation. Aspects and applications of a class of models are explored by simulation, in the context of both individual differences twin analysis and, in a companion paper (Purcell & Sham, 2002) sibpair quantitative trait locus linkage analysis. As well as elucidating environmental pathways, consideration of gene-environment interaction in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12573187 r1369-0523 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Twin Study12573187Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College, London, UK. s.purcell@iop.kcl.ac.uk~?3Purcell, S. Cherny, S. S. Hewitt, J. K. Sham, P. C.2001UOptimal sibship selection for genotyping in quantitative trait locus linkage analysis1-13 Hum Hered521Chromosome Mapping *Genetic Techniques Genotype Humans *Linkage (Genetics) Models, Genetic Nuclear Family *Quantitative Trait, HeritablelIn this paper we present a novel method for selecting optimally informative sibships of any size for quantitative trait locus (QTL) linkage analysis. The method allocates a quantitative index of potential informativeness to each sibship on the basis of observed trait scores and an assumed true QTL model. Any sample of phenotypically screened sibships can therefore be easily rank-ordered for selective genotyping. The quantitative index is the sibship's expected contribution to the non-centrality parameter. This expectation represents the weighted sum of chi(2) test statistics that would be obtained given the observed trait values over all possible sibship genotypic configurations; each configuration is weighted by the likelihood of it occurring given the assumed true genetic model. The properties of this procedure are explored in relation to the accuracy of the assumed true genetic model and sibship size. In comparison to previous methods of selecting phenotypically extreme sibships for genotyping, the proposed method is considerably more efficient and is robust with regard to the specification of the genetic model.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11359064 g0001-5652 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11359064~Social, Genetic and Developmental Research Centre, 111 Denmark Hill, Denmark Hill, London SE5 8AF, UK. s.purcell@iop.kcl.ac.ukI~? %Purcell, S. Cherny, S. S. Sham, P. C.2003eGenetic Power Calculator: design of linkage and association genetic mapping studies of complex traits149-50Bioinformatics191 Chromosome Mapping/*methods Gene Frequency/*genetics Genetic Markers/genetics Genetic Predisposition to Disease/genetics *Internet Linkage (Genetics)/*genetics Linkage Disequilibrium/genetics Models, Genetic *Models, Statistical Pedigree Quantitative Trait, Heritable Sample Size SoftwareJanSUMMARY: A website for performing power calculations for the design of linkage and association genetic mapping studies of complex traits. AVAILABILITY: The package is made available athttp://statgen.iop.kcl.ac.uk/gpc/.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12499305 g1367-4803 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.12499305Social, Genetics and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College London, De Crespigny Park, UK. s.purcell@iop.kcl.ac.uk~?!#Purcell, S. Daly, M. J. Sham, P. C.2007*WHAP: haplotype-based association analysis255-6Bioinformatics232jAlgorithms Base Sequence Chromosome Mapping/*methods Genetic Markers/genetics Genetics, Population Haplotypes/*genetics Linkage Disequilibrium/*genetics Molecular Sequence Data Polymorphism, Single Nucleotide/*genetics Quantitative Trait Loci/*genetics Sequence Alignment/methods Sequence Analysis, DNA/*methods *Software Statistics Variation (Genetics)/geneticsJan 15We describe a software tool to perform haplotype-based association analysis, for quantitative and qualitative traits, in population and family samples, using single nucleotide polymorphism or multiallelic marker data. A range of tests is offered: omnibus and haplotype-specific tests; prospective and retrospective likelihoods; covariates and moderators; sliding window analyses; permutation P-values. We focus on the ability to flexibly impose constraints on haplotype effects, which allows for a range of conditional haplotype-based likelihood ratio tests: for example, whether an allele has an effect independent of its haplotypic background, or whether a single variant can explain the overall association at a locus. We illustrate using these tests to dissect a multi-locus association. AVAILABILITY: WHAP is a C/C++ program, freely available from the author's website: http://pngu.mgh.harvard.edu/purcell/whap/fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17118959 l1460-2059 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't17118959SCenter for Human Genetic Research, MGH, Boston, MA, USA. shaun@pngu.mgh.harvard.edu4~?"Purcell, S. Neale, B. Todd-Brown, K. Thomas, L. Ferreira, M. A. Bender, D. Maller, J. Sklar, P. de Bakker, P. I. Daly, M. J. Sham, P. C.2007TPLINK: a tool set for whole-genome association and population-based linkage analyses559-75Am J Hum Genet813SepWhole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17701901 g0002-9297 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't17701901tCenter for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA. shaun@pngu.mgh.harvard.edu0~?#Purcell, S. Sham, P.2002hVariance components models for gene-environment interaction in quantitative trait locus linkage analysis572-6Twin Res56Computer Simulation *Environment Genetics, Behavioral Humans *Linkage (Genetics) *Models, Genetic Quantitative Trait Loci/*genetics Twins/*geneticsDeclGene-environment interaction (G x E) is likely to be a common and important source of variation for complex behavioral traits. Gene-environment interaction, or genetic control of sensitivity to the environment, can be incorporated into variance components twin and sib-pair analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. An approach described in a companion paper (Purcell, 2002) is applied to sib-pair variance components linkage analysis in two ways: allowing for quantitative trait locus by environment interaction and utilizing information on any residual interactions detected prior to analysis. As well as elucidating environmental pathways, consideration of G x E in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12573188 r1369-0523 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Twin Study12573188Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College, London, UK. s.purcell@iop.kcl.ac.uk~?$Purcell, S. Sham, P.2004VProperties of structured association approaches to detecting population stratification93-107 Hum Hered582Algorithms Computer Simulation Data Interpretation, Statistical Gene Frequency Genetics, Population/*statistics & numerical data Humans Linkage Disequilibrium|OBJECTIVE: To examine the properties of the structured association approach for the detection and correction of population stratification. METHOD: A method is developed, within a latent class analysis framework, similar to the methods proposed by Satten et al. (2001) and Pritchard et al. (2000). A series of simulations illustrate the relative impact of number and type of loci, sample size and population structure. RESULTS: The ability to detect stratification and assign individuals to population strata is determined for a number of different scenarios. CONCLUSION: The results underline the importance of careful marker selection.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15711089 !0001-5652 (Print) Journal Article15711089cWhitehead Institute, Nine Cambridge Center, Cambridge, MA 02129, USA. soyrcekk@pngu.mgh.harvard.eduT~?%,Putter, H. Lebrec, J. van Houwelingen, H. C.20034Selection strategies for linkage studies using twins377-82Twin Res65cHumans Linkage (Genetics)/*genetics Mathematics Models, Theoretical Random Allocation *Twin StudiesOct3Genetic linkage analysis for complex diseases offers a major challenge to geneticists. In these complex diseases multiple genetic loci are responsible for the disease and they may vary in the size of their contribution; the effect of any single one of them is likely to be small. In many situations, like in extensive twin registries, trait values have been recorded for a large number of individuals, and preliminary studies have revealed summary measures for those traits, like mean, variance and components of variance, including heritability. Given the small effect size, a random sample of twins will require a prohibitively large sample size. It is well known that selective sampling is far more efficient in terms of genotyping effort. In this paper we derive easy expressions for the information contributed by sib pairs for the detection of linkage to a quantitative trait locus (QTL). We consider random samples as well as samples of sib pairs selected on the basis of their trait values. These expressions can be rapidly computed and do not involve simulation. We extend our results for quantitative traits to dichotomous traits using the concept of a liability threshold model. We present tables with required sample sizes for height, insulin levels and migraine, three of the traits studied in the GenomEUtwin project.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14624721 !1369-0523 (Print) Journal Article14624721Department of Medical Statistics, Leiden University Medical Center, University of Leiden, Leiden, The Netherlands. h.putter@lumc.nlE~?&2Putter, H. Sandkuijl, L. A. van Houwelingen, J. C.20027Score test for detecting linkage to quantitative traits345-55Genet Epidemiol224Chromosome Mapping/*methods Genotype Humans Likelihood Functions Linkage (Genetics)/*genetics Models, Genetic Nuclear Family *Quantitative Trait, HeritableAprThe two most popular methods to detect linkage of a quantitative trait to a marker are the Haseman-Elston regression method and the variance components likelihood-ratio test. In the literature, these methods are frequently compared and the relative advantages and disadvantages of each method are well known. In this article, we derive a score test for the variance component attributable to a specific quantitative trait locus and show that for sib-pairs it is mathematically equivalent to a recently proposed version of the Haseman-Elston method that optimally combines the sum squared and the difference squared of the centered phenotype values of the sibs. Because score tests and likelihood-ratio tetsts are equivalent for large sample sizes, the variance components likelihood-ratio test is also asymptotically equivalent to this optimal Haseman-Elston test. This fact gives a theoretical explanation of the empirical observation from simulation studies reporting similar power of the variance components likelihood-ratio test and the optimal Haseman-Elston method. Perhaps more importantly for practical purposes, the score test can also be extended in a natural way to support the simultaneous analysis of more than two subjects and multivariate phenotypes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11984866 !0741-0395 (Print) Journal Article11984866mDepartment of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands. h.putter@lumc.nl~?'Qian, D. Beckmann, L.2002,Minimum-recombinant haplotyping in pedigrees1434-45Am J Hum Genet706*Algorithms Ataxia/*genetics *Computer Simulation Female Gene Order/genetics Genotype Haplotypes/*genetics Humans Male Nuclear Family Pedigree Recombination, Genetic/*genetics Research DesignJunThis article presents a six-rule algorithm for the reconstruction of multiple minimum-recombinant haplotype configurations in pedigrees. The algorithm has three major features: First, it allows exhaustive search of all possible haplotype configurations under the criterion that there are minimum recombinants between markers. Second, its computational requirement is on the order of O(J(2)L(3)) in current implementation, where J is the family size and L is the number of marker loci under analysis. Third, it applies to various pedigree structures, with and without consanguinity relationship, and allows missing alleles to be imputed, during the haplotyping process, from their identical-by-descent copies. Haplotyping examples are provided using both published and simulated data sets.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11992251 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11992251Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, CHP 218, Los Angeles, CA 90089, USA. gqian@usc.edu~?(Qin, Z. S. Niu, T. Liu, J. S.2002rPartition-ligation-expectation-maximization algorithm for haplotype inference with single-nucleotide polymorphisms1242-7Am J Hum Genet715_*Algorithms Genetic Markers *Haplotypes Linkage Disequilibrium *Polymorphism, Single NucleotideNovfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12452179 n0002-9297 (Print) Comment Letter Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.12452179e?)R Development Core Team,2005RVienna&R Foundation for Statistical Computing~?*Rabinowitz, D.1997>A transmission disequilibrium test for quantitative trait loci342-50 Hum Hered476Alleles Chromosome Mapping/*methods Computer Simulation *Genetics, Population Humans *Linkage Disequilibrium Probability *Quantitative Trait, HeritableNov-DecThe transmission disequilibrium test uses association between marker alleles and dichotomous traits for precise genetic mapping while avoiding confounding due to population admixture. Here, the methodology is generalized from dichotomous traits to quantitative traits. The generalization is computationally straightforward and may be used with multiple alleles and with siblings. Parametric assumptions on the distribution of the quantitative traits are not needed. Environmental and demographic covariates may be incorporated into the analysis. The results of simulation studies that provide information about the power of the approach are reported.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9391826 F0001-5652 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9391826]Department of Statistics, Columbia University, New York, NY 10027, USA. dan@stat.columbia.edu H~?+'Redon, R. Ishikawa, S. Fitch, K. R. Feuk, L. Perry, G. H. Andrews, T. D. Fiegler, H. Shapero, M. H. Carson, A. R. Chen, W. Cho, E. K. Dallaire, S. Freeman, J. L. Gonzalez, J. R. Gratacos, M. Huang, J. Kalaitzopoulos, D. Komura, D. MacDonald, J. R. Marshall, C. R. Mei, R. Montgomery, L. Nishimura, K. Okamura, K. Shen, F. Somerville, M. J. Tchinda, J. Valsesia, A. Woodwark, C. Yang, F. Zhang, J. Zerjal, T. Zhang, J. Armengol, L. Conrad, D. F. Estivill, X. Tyler-Smith, C. Carter, N. P. Aburatani, H. Lee, C. Jones, K. W. Scherer, S. W. Hurles, M. E.20063Global variation in copy number in the human genome444-54Nature4447118Chromosome Mapping Gene Dosage Genetics, Population *Genome, Human Genomics/methods Genotype Humans Linkage Disequilibrium Molecular Diagnostic Techniques Oligonucleotide Array Sequence Analysis/methods Polymorphism, Single Nucleotide *Variation (Genetics)Nov 23Copy number variation (CNV) of DNA sequences is functionally significant but has yet to be fully ascertained. We have constructed a first-generation CNV map of the human genome through the study of 270 individuals from four populations with ancestry in Europe, Africa or Asia (the HapMap collection). DNA from these individuals was screened for CNV using two complementary technologies: single-nucleotide polymorphism (SNP) genotyping arrays, and clone-based comparative genomic hybridization. A total of 1,447 copy number variable regions (CNVRs), which can encompass overlapping or adjacent gains or losses, covering 360 megabases (12% of the genome) were identified in these populations. These CNVRs contained hundreds of genes, disease loci, functional elements and segmental duplications. Notably, the CNVRs encompassed more nucleotide content per genome than SNPs, underscoring the importance of CNV in genetic diversity and evolution. The data obtained delineate linkage disequilibrium patterns for many CNVs, and reveal marked variation in copy number among populations. We also demonstrate the utility of this resource for genetic disease studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17122850 G1476-4687 (Electronic) Journal Article Research Support, Non-U.S. Gov't17122850cThe Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. o~?,Reich, D. E. Cargill, M. Bolk, S. Ireland, J. Sabeti, P. C. Richter, D. J. Lavery, T. Kouyoumjian, R. Farhadian, S. F. Ward, R. Lander, E. S.2001*Linkage disequilibrium in the human genome199-204Nature4116834Alleles Bias (Epidemiology) Chromosome Mapping/*methods Computer Simulation Continental Population Groups/genetics Europe/ethnology Founder Effect Genetic Diseases, Inborn/genetics *Genome, Human Haplotypes/genetics Heterozygote Humans Linkage Disequilibrium/*genetics Models, Genetic Nigeria Phylogeny Polymorphism, Single Nucleotide/*genetics Recombination, Genetic/genetics Reproducibility of Results Selection (Genetics) Time Factors United StatesMay 10 With the availability of a dense genome-wide map of single nucleotide polymorphisms (SNPs), a central issue in human genetics is whether it is now possible to use linkage disequilibrium (LD) to map genes that cause disease. LD refers to correlations among neighbouring alleles, reflecting 'haplotypes' descended from single, ancestral chromosomes. The size of LD blocks has been the subject of considerable debate. Computer simulations and empirical data have suggested that LD extends only a few kilobases (kb) around common SNPs, whereas other data have suggested that it can extend much further, in some cases greater than 100 kb. It has been difficult to obtain a systematic picture of LD because past studies have been based on only a few (1-3) loci and different populations. Here, we report a large-scale experiment using a uniform protocol to examine 19 randomly selected genomic regions. LD in a United States population of north-European descent typically extends 60 kb from common alleles, implying that LD mapping is likely to be practical in this population. By contrast, LD in a Nigerian population extends markedly less far. The results illuminate human history, suggesting that LD in northern Europeans is shaped by a marked demographic event about 27,000-53,000 years ago.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11346797 0028-0836 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.11346797Whitehead Institute / MIT Center for Genome Research, Nine Cambridge Center, Cambridge, Massachusetts 02142, USA. reich@genome.wi.mit.eduP~?-Reich, D. E. Lander, E. S.2001(On the allelic spectrum of human disease502-10 Trends Genet179*Alleles Chromosome Mapping Gene Frequency Genes, Recessive *Genetic Predisposition to Disease Humans Linkage (Genetics) Models, Genetic Mutation Predictive Value of Tests Selection (Genetics) Variation (Genetics) X ChromosomeSepHuman disease genes show enormous variation in their allelic spectra; that is, in the number and population frequency of the disease-predisposing alleles at the loci. For some genes, there are a few predominant disease alleles. For others, there is a wide range of disease alleles, each relatively rare. The allelic spectrum is important: disease genes with only a few deleterious alleles can be more readily identified and are more amenable to clinical testing. Here, we weave together strands from the human mutation and population genetics literature to provide a framework for understanding and predicting the allelic spectra of disease genes. The theory does a reasonable job for diseases where the genetic etiology is well understood. It also has bearing on the Common Disease/Common Variants (CD/CV) hypothesis, predicting that at loci where the total frequency of disease alleles is not too small, disease loci will have relatively simple spectra.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11525833 30168-9525 (Print) Comparative Study Journal Article11525833The Whitehead Institute/MIT Center for Genome Research, Nine Cambidge Center, Cambridge, MA 02142, USA. reich@genome.wi.mit.edu~?.~Reich, D. E. Schaffner, S. F. Daly, M. J. McVean, G. Mullikin, J. C. Higgins, J. M. Richter, D. J. Lander, E. S. Altshuler, D.2002]Human genome sequence variation and the influence of gene history, mutation and recombination135-42 Nat Genet321Animals Computer Simulation *Evolution, Molecular *Genome, Human Humans Linkage Disequilibrium *Mutation Pan troglodytes/genetics Polymorphism, Single Nucleotide *Recombination, Genetic *Variation (Genetics)SepVariation in the human genome sequence is key to understanding susceptibility to disease in modern populations and the history of ancestral populations. Unlocking this information requires knowledge of the patterns and underlying causes of human sequence diversity. By applying a new population-genetic framework to two genome-wide polymorphism surveys, we find that the human genome contains sizeable regions (stretching over tens of thousands of base pairs) that have intrinsically high and low rates of sequence variation. We show that the primary determinant of these patterns is shared genealogical history. Only a fraction of the variation (at most 25%) is due to the local mutation rate. By measuring the average distance over which genealogical histories are typically preserved, these data provide the first genome-wide estimate of the average extent of correlation among variants (linkage disequilibrium). The results are best explained by extreme variability in the recombination rate at a fine scale, and provide the first empirical evidence that such recombination 'hot spots' are a general feature of the human genome and have a principal role in shaping genetic variation in the human population.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12161752 1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.12161752lWhitehead Institute/MIT Center for Genome Research, One Kendall Square, Cambridge, Massachusetts 02139, USA.~?/Rice, K. M. Holmans, P.2003KAllowing for genotyping error in analysis of unmatched case-control studies165-74 Ann Hum Genet67Pt 2Case-Control Studies *Genotype Heterozygote Homozygote Humans Likelihood Functions Models, Statistical Polymorphism, Single Nucleotide Reproducibility of Results Risk Statistics/*methodsMarA commonly-used method for testing for association between disease and a single-nucleotide polymorphism (SNP) is to compare the frequencies of the SNP genotypes in a sample of unrelated cases to those in a sample of unrelated controls drawn from the same population (an unmatched case-control study). A drawback of such a study is that it is impossible to detect genotyping errors, and few methods have been developed to allow for the presence of undetected genotyping errors. In this paper, we obtain analytic formulae for estimates of genotypic relative risks in terms of error probability (e). In general, e will be unknown. We investigate the effect of assuming both correct and incorrect values of e on power and type I error, and also on the genotypic relative risk estimates. The choice of e was found to have no effect on power or Type I error probability (provided a 2df test was used, allowing relative risks of homozygotes and heterozygotes to differ). However, overestimating e in the presence of a true association was found in general to bias relative risk estimates away from the null, with underestimates of e having the opposite effect. Although e is unknown, it may be estimated from an external "validation" study, such as genotyping a sample of unrelated individuals twice and counting the discrepancies. Simulation results suggest that, for such a study, 25 individuals would be sufficient to give approximately unbiased estimates of relative risks.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12675691 !0003-4800 (Print) Journal Article12675691&MRC Biostatistics Unit, Cambridge, UK. X~?08Rietveld, M. J. Posthuma, D. Dolan, C. V. Boomsma, D. I.2003JADHD: sibling interaction or dominance: an evaluation of statistical power247-55 Behav Genet333Analysis of Variance Attention Deficit Disorder with Hyperactivity/*genetics *Dominance-Subordination Humans *Interpersonal Relations Models, Statistical Reproducibility of Results *Siblings Twins, Dizygotic/*genetics Twins, Monozygotic/*geneticsMaySibling interaction effects are suggested by a difference in phenotypic variance between monozygotic (MZ) twins and dizygotic (DZ) twins, and a pattern of twin correlations that is inconsistent with additive genetic influences. Notably, negative sibling interaction will result in MZ correlations which are more than twice as high as DZ correlations, a pattern also seen in the presence of genetic dominance. Negative sibling interaction effects have been reported in most genetic studies on Attention Deficit Hyperactivity Disorder (ADHD) and related phenotypes, while the presence of genetic dominance is not always considered in these studies. In the present paper the statistical power to detect both negative sibling interaction effects and genetic dominance is explored. Power calculations are presented for univariate models including sources of variation due to additive genetic influences, unique environmental influences, dominant genetic influences and a negative sibling interaction (i.e., contrast effect) between phenotypes of twins. Parameter values for heritability and contrast effects are chosen in accordance with published behavior genetic studies on ADHD and associated phenotypes. Results show that when both genetic dominance and contrast effects are truly present and using a classical twin design, genetic dominance is more likely to go undetected than the contrast effect. Failure to detect the presence of genetic dominance consequently gives rise to slightly biased estimates of additive genetic effects, unique environmental effects, and the contrast effect. Contrast effects are more easily detected in the absence of genetic dominance. If the significance of the contrast effect is evaluated while also including genetic dominance, small contrast effects are likely to go undetected, resulting in a relatively large bias in estimates of the other parameters. Alternative genetic designs, such as adding pairs of unrelated siblings reared together to a classical twin design, or adding non-twin siblings to twin pairs, greatly enhances the statistical power to detect contrast effects as well as the power to distinguish between genetic dominance and contrast effects.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12837015 !0001-8244 (Print) Journal Article12837015kDepartment of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands. mjh.rietveld@psy.vu.nl~?1)Rijsdijk, F. V. Hewitt, J. K. Sham, P. C.2001FAnalytic power calculation for QTL linkage analysis of small pedigrees335-40Eur J Hum Genet95s*Analysis of Variance Female Humans Linkage (Genetics) Male Models, Genetic Pedigree *Quantitative Trait, HeritableMayPower calculation for QTL linkage analysis can be performed via simple algebraic formulas for small pedigrees, but requires intensive computation for large pedigrees, in order to evaluate the expectation of the test statistic over all possible inheritance vectors at the test position. In this report, we show that the non-centrality parameter for an arbitrary pedigree can be approximated by the sum of the variances of the correlations between all pairs of relatives, each variance being weighted by a factor that is determined by the mean correlation of the pair. We show that this approximation is sufficiently accurate for practical purposes in small to moderately large pedigrees, and that large sibships are more efficient than other family structures under a range of genetic models.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11378821 g1018-4813 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11378821Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, London SE5 8AF, UK. spjgfvr@iop.kcl.ac.uk~?2Rijsdijk, F. V. Sham, P. C.2002gEstimation of sib-pair IBD sharing and multipoint polymorphism information content by linear regression211-20 Behav Genet323Alleles Chromosome Mapping Gene Frequency/genetics Genetic Markers/*genetics *Genotype Humans Markov Chains *Models, Genetic *Nuclear Family Polymorphism, Genetic/*genetics *Quantitative Trait, Heritable Regression AnalysisMayA simple method of estimating IBD sharing (pi) of sibling pairs from multipoint genotype data based on linear regression is presented. The new method is an extension of that developed by Fulker, Cherny, and Cardon (1995) and involves a measure of sib-pair specific information, W, defined as the difference between the unconditional variance of pi and the conditional variance of pi given the marker genotype data. When markers are not fully informative, the W method provides estimates closer to those obtained from the exact hidden Markov method (HMM) than the original Fulker method. Using W, we also derive a generalisation for the polymorphism information content (PIC) for multiple markers. This multipoint polymorphism information content (MPIC) can be evaluated at any location along a marker map and is proportional to the noncentrality parameter for linkage. We illustrate MPIC by assessing the relative information content of single nucleotide polymorphism (SNP) and microsatellite markers.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12141782 z0001-8244 (Print) Evaluation Studies Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.12141782eSocial, Genetic & Developmental Psychiatry Research Centre, IoP, London, UK. f.rijsdijk@iop.kcl.ac.uk~?3Riordan, J. R. Rommens, J. M. Kerem, B. Alon, N. Rozmahel, R. Grzelczak, Z. Zielenski, J. Lok, S. Plavsic, N. Chou, J. L. et al.,1989]Identification of the cystic fibrosis gene: cloning and characterization of complementary DNA1066-73Science2454922Amino Acid Sequence Base Sequence Biological Transport Cloning, Molecular/methods Cystic Fibrosis/*genetics/metabolism/pathology Cystic Fibrosis Transmembrane Conductance Regulator DNA/*isolation & purification *Genes *Genes, Recessive Humans Ion Channels/pathology Membrane Proteins/*genetics/isolation & purification Molecular Sequence Data Peptides/*genetics/isolation & purification Sequence Homology, Nucleic Acid Transcription, GeneticSep 8Overlapping complementary DNA clones were isolated from epithelial cell libraries with a genomic DNA segment containing a portion of the putative cystic fibrosis (CF) locus, which is on chromosome 7. Transcripts, approximately 6500 nucleotides in size, were detectable in the tissues affected in patients with CF. The predicted protein consists of two similar motifs, each with (i) a domain having properties consistent with membrane association and (ii) a domain believed to be involved in ATP (adenosine triphosphate) binding. A deletion of three base pairs that results in the omission of a phenylalanine residue at the center of the first predicted nucleotide-binding domain was detected in CF patients.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2475911 g0036-8075 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.2475911QDepartment of Biochemistry, Hospital for Sick Children, Toronto, Ontario, Canada.~?4 Risch, N.1990Linkage strategies for genetically complex traits. III. The effect of marker polymorphism on analysis of affected relative pairs242-53Am J Hum Genet462Female *Genetics, Medical Humans *Linkage (Genetics) Lod Score Male *Models, Genetic *Models, Statistical Pedigree Polymorphism, Genetic ProbabilityFebgThe results from the second paper of this series are reexamined for markers that are not completely polymorphic. A maximum lod score (MLS) criterion is defined for affected relative pairs. The expected MLS (EMLS) is calculated as a function of the marker polymorphic information content (PIC) for various values of lambda R (relative risk ratio) and different relative types by using simulations. An m-allele model with equal allele frequencies is employed. The EMLS is calculated for two sampling strategies: scheme 1, which uses pairs only, and scheme 2, which also includes additional informative relatives. For scheme 2, the percent of the maximum achievable EMLS (i.e., for a marker with a PIC of 1.0) is approximately equal to the marker PIC value for all relative types. For scheme 1, the EMLS is greatly diminished unless PIC is high, especially for distant relatives. For example, scheme 1 is not cost-effective for sibs unless PIC greater than .7; for second- and third-degree relatives, PIC must be greater than .85. Therefore, in general, it will be worthwhile to type additional relatives in linkage studies using affected pairs. The comparative value of sibs versus distant relatives depends on lambda R, recombination theta, and PIC. For large lambda R and PIC values, distant relatives are preferred. Alternatively, for smaller lambda R and PIC values, sibs are best.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2301394 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.2301394fDepartment of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06510.~?5 Risch, N.1990[Linkage strategies for genetically complex traits. II. The power of affected relative pairs229-41Am J Hum Genet462Alleles Female Genetic Diseases, Inborn/*genetics *Genetics, Medical Humans *Linkage (Genetics) Male *Models, Genetic *Models, Statistical Pedigree Probability Recombination, GeneticFeb/The power to detect disease-susceptibility loci through linkage analysis using pairs of affected relatives and affected-unaffected pairs is examined. Allelic identity by descent (ibd) for a completely polymorphic marker for sibling, uncle-nephew, grandparent-grandchild, half-sib, and first-cousin pairs is considered. Affected-unaffected pairs generally represent a poor strategy. For single-locus models, ibd depends on lambda R, the risk ratio for type R relatives compared with population prevalence, and the recombination fraction theta. The ibd for grandparent-grandchild pairs is least affected by recombination, followed by sibs, half-sib, uncle-nephew, and first-cousin pairs. For diseases with large lambda values and for small theta values, distant relatives offer greater power. For larger theta values, grandparent-grandchild pairs are best; for small lambda values, sibs are best. Additive and multiplicative multilocus models are considered. For the multiplicative model, the same formulas as in the single-locus model apply, except that lambda iR (for the ith contributing locus) is substituted for lambda R. For the additive model, the deviation from null expectation for ibd is divided among all contributing loci. Compared with the multiplicative model, for an additive model there is usually greater advantage in distant relationships. Multipoint analysis using linked marker loci for affected relative pairs is described. Simultaneous use of multiple markers diminishes the effect of recombination and allows for localization of the disease-susceptibility locus.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2301393 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.2301393fDepartment of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06510.~?6 Risch, N.1990GLinkage strategies for genetically complex traits. I. Multilocus models222-8Am J Hum Genet462v*Genetics, Medical Humans *Linkage (Genetics) Mathematics *Models, Genetic *Models, Statistical Schizophrenia/geneticsFebIn order to investigate linkage detection strategies for genetically complex traits, multilocus models of inheritance need to be specified. Here, two types of multilocus model are described: (1) a multiplicative model, representing epistasis (interaction) among loci, and (2) an additive model, which is shown to closely approximate genetic heterogeneity, which is characterized by no interlocus interaction. A ratio lambda R of risk for type R relatives that is compared with population prevalence is defined. For a single-locus model, lambda R - 1 decreases by a factor of two with each degree of relationship. The same holds true for an additive multilocus model. For a multiplicative (epistasis) model, lambda R - 1 decreases more rapidly than by a factor of two with degree of relationship. Examination of lambda R values for various classes of relatives can potentially suggest the presence of multiple loci and epistasis. For example, data for schizophrenia suggest multiple loci in interaction. It is shown in the second paper of this series that lambda R is the critical parameter in determining power to detect linkage by using affected relative pairs.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2301392 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.2301392fDepartment of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06510.~?7 Risch, N.1992Corrections to "Linkage strategies for genetically complex traits. III. The effect of marker polymorphism on analysis of affected relative pairs"673-5Am J Hum Genet513]Genetic Markers/*genetics Humans Linkage (Genetics)/*genetics Polymorphism, Genetic/*geneticsSepehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1496996 O0002-9297 (Print) Letter Published Erratum Research Support, U.S. Gov't, P.H.S.1496996J~?8Risch, N. Merikangas, K.19967The future of genetic studies of complex human diseases1516-7Science2735281Alleles Genetic Diseases, Inborn/*genetics *Genetic Predisposition to Disease *Genetic Techniques Genome, Human Genotype Heterozygote Humans Linkage (Genetics) Polymorphism, Genetic ProbabilitySep 13ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8801636 !0036-8075 (Print) Journal Article8801636]Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-5120, USA. ~?9Risch, N. Teng, J.1998The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases I. DNA pooling1273-88 Genome Res812Case-Control Studies DNA/*analysis Genetic Diseases, Inborn/*genetics Humans Linkage Disequilibrium/*genetics *Models, Genetic Pedigree Research DesignDecWe consider statistics for analyzing a variety of family-based and nonfamily-based designs for detecting linkage disequilibrium of a marker with a disease susceptibility locus. These designs include sibships with parents, sibships without parents, and use of unrelated controls. We also provide formulas for and evaluate the relative power of different study designs using these statistics. In this first paper in the series, we derive statistical tests based on data derived from DNA pooling experiments and describe their characteristics. Although designs based on affected and unaffected sibs without parents are usually robust to population stratification, they suffer a loss of power compared with designs using parents or unrelateds as controls. Although increasing the number of unaffected sibs improves power, the increase is generally not substantial. Designs including sibships with multiple affected sibs are typically the most powerful, with any of these control groups, when the disease allele frequency is low. When the allele frequency is high, however, designs with unaffected sibs as controls do not retain this advantage. In designs with parents, having an affected parent has little impact on the power, except for rare dominant alleles, where the power is increased compared with families with no affected parents. Finally, we also demonstrate that for sibships with parents, only the parents require individual genotyping to derive the TDT statistic, whereas all the offspring can be pooled. This can potentially lead to considerable savings in genotyping, especially for multiplex sibships. The formulas and tables we derive should provide some guidance to investigators designing nuclear family-based linkage disequilibrium studies for complex diseases.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9872982 n1088-9051 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Review9872982zDepartment of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA. Risch@lahmed.stanford.edu ~?:Risch, N. Teng, J.1999The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases II. Individual genotyping234-241 Genome Res9Case-Control Studies DNA/*analysis Genetic Diseases, Inborn/*genetics Humans Linkage Disequilibrium/*genetics *Models, Genetic Pedigree Research DesignDecWe consider statistics for analyzing a variety of family-based and nonfamily-based designs for detecting linkage disequilibrium of a marker with a disease susceptibility locus. These designs include sibships with parents, sibships without parents, and use of unrelated controls. We also provide formulas for and evaluate the relative power of different study designs using these statistics. In this first paper in the series, we derive statistical tests based on data derived from DNA pooling experiments and describe their characteristics. Although designs based on affected and unaffected sibs without parents are usually robust to population stratification, they suffer a loss of power compared with designs using parents or unrelateds as controls. Although increasing the number of unaffected sibs improves power, the increase is generally not substantial. Designs including sibships with multiple affected sibs are typically the most powerful, with any of these control groups, when the disease allele frequency is low. When the allele frequency is high, however, designs with unaffected sibs as controls do not retain this advantage. In designs with parents, having an affected parent has little impact on the power, except for rare dominant alleles, where the power is increased compared with families with no affected parents. Finally, we also demonstrate that for sibships with parents, only the parents require individual genotyping to derive the TDT statistic, whereas all the offspring can be pooled. This can potentially lead to considerable savings in genotyping, especially for multiplex sibships. The formulas and tables we derive should provide some guidance to investigators designing nuclear family-based linkage disequilibrium studies for complex diseases.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9872982 n1088-9051 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Review9872982zDepartment of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA. Risch@lahmed.stanford.eduh~?;Risch, N. Zhang, H.1995JExtreme discordant sib pairs for mapping quantitative trait loci in humans1584-9Science2685217Alleles Chromosome Mapping/*methods/statistics & numerical data *Chromosomes, Human Genotype Humans *Linkage (Genetics) Models, Genetic Models, Statistical Nuclear Family Phenotype Probability Sample SizeJun 16Analysis of differences between siblings (sib pair analysis) is a standard method of genetic linkage analysis for mapping quantitative trait loci, such as those contributing to hypertension and obesity, in humans. In traditional designs, pairs are selected at random or with one sib having an extreme trait value. The majority of such pairs provide little power to detect linkage; only pairs that are concordant for high values, low values, or extremely discordant pairs (for example, one in the top 10 percent and the other in the bottom 10 percent of the distribution) provide substantial power. Focus on discordant pairs can reduce the amount of genotyping necessary over conventional designs by 10- to 40-fold.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7777857 F0036-8075 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.7777857pDepartment of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA.|~?< Risch, N. J.20008Searching for genetic determinants in the new millennium847-56Nature4056788Biotechnology Computational Biology Genetic Diseases, Inborn/genetics Genetic Predisposition to Disease Genetics, Medical/*trends *Genome, Human HumansJun 15^Human genetics is now at a critical juncture. The molecular methods used successfully to identify the genes underlying rare mendelian syndromes are failing to find the numerous genes causing more common, familial, non-mendelian diseases. With the human genome sequence nearing completion, new opportunities are being presented for unravelling the complex genetic basis of non-mendelian disorders based on large-scale genome-wide studies. Considerable debate has arisen regarding the best approach to take. In this review I discuss these issues, together with suggestions for optimal post-genome strategies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10866211 (0028-0836 (Print) Journal Article Review10866211[Department of Genetics, Stanford University School of Medicine, California 94305-5120, USA.F~?=Risch, N. J. Zhang, H.1996ZMapping quantitative trait loci with extreme discordant sib pairs: sampling considerations836-43Am J Hum Genet584Analysis of Variance Chromosome Mapping/*statistics & numerical data Genetic Screening Humans *Linkage (Genetics) Models, Genetic *Models, Statistical Nuclear Family Sample SizeAprElsewhere we have proposed the use of extreme discordant sib pairs (EDSPs) for mapping quantitative trait loci in humans. Here we present sample sizes necessary to achieve a given level of power with this study design, as well as the number of sibs that need to be screened to obtain the required sample. Further, we present simple formulas for adjusting sample sizes to account for variable significance levels and power, as well as the density and informativeness of linkage markers in a multipoint sib-pair analysis. We conclude that with EDSPs, the most powerful study design, the smallest genetic effect detectable with a realistic sample size is approximately 10% of the variance of the trait.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8644748 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8644748eDepartment of Genetics, Stanford University School of Medicine, Stanford, California 94305-5120, USA.~?>FRoberts, S. B. MacLean, C. J. Neale, M. C. Eaves, L. J. Kendler, K. S.1999cReplication of linkage studies of complex traits: an examination of variation in location estimates876-84Am J Hum Genet653Chromosome Mapping/*methods Chromosomes, Human/genetics *Computer Simulation Gene Frequency Genetic Diseases, Inborn/*genetics Genetic Heterogeneity Genetic Markers Genetic Predisposition to Disease Genotype Humans Likelihood Functions Linkage (Genetics)/*genetics Lod Score Multifactorial Inheritance/*genetics Penetrance Phenotype Sample Size Sensitivity and Specificity Statistical DistributionsSep In linkage studies, independent replication of positive findings is crucial in order to distinguish between true positives and false positives. Recently, the following question has arisen in linkage studies of complex traits: at what distance do we reject the hypothesis that two location estimates in a genomic region represent the same gene? Here we attempt to address this question. Sampling distributions for location estimates were constructed by computer simulation. The conditions for simulation were chosen to reflect features of "typical" complex traits, including incomplete penetrance, phenocopies, and genetic heterogeneity. Our findings, which bear on what is considered a replication in linkage studies of complex traits, suggest that, even with relatively large numbers of multiplex families, chance variation in the location estimate is substantial. In addition, we report evidence that, for the conditions studied here, the standard error of a location estimate is a function of the magnitude of the expected LOD score.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10441592 !0002-9297 (Print) Journal Article10441592Department of Human Genetics, Virginia Institute of Psychiatric and Behavioral Genetics, Medical College of Virginia of Virginia Commonwealth University, Richmond, VA 23298-0126, USA.~??Robin, E. D. Wong, R.1988ZMitochondrial DNA molecules and virtual number of mitochondria per cell in mammalian cells507-13J Cell Physiol1363Animals DNA Probes DNA, Mitochondrial/*analysis Female Humans Macrophages/ultrastructure Methods Mice Mitochondria/*ultrastructure Muscles/ultrastructure Plasmids Rabbits RatsSepdA new biochemical method for estimating the virtual number of mitochondria (mt) per cell was developed and used together with a plasmid probe to measure mt DNA/mitochondrion and mt DNA/cell. These methods were used in five cell types from four mammalian species. Mt DNA/mitochondrion was essentially constant in all cell types (mean 2.6 +/- 0.30 SE mitochondrial DNA molecules/mt). Mt DNA molecules/cell encompassed an eight-fold range between various cell types (low 220 +/- 6.2; high 1,720 +/- 162 mt DNA molecules/cell). Virtual mt number/cell ranged from 83 +/- 17 to 677 +/- 80 (SE) mt/cell in various cell types. All five mammalian virtual mitochondria contained the same genomic mass. The number of virtual mitochondria per cell and amount of mt DNA per cell appear to be closely regulated within a given cell type but differ widely from cell type to cell type.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3170646 g0021-9541 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.3170646QDepartment of Medicine, Stanford University School of Medicine, California 94305. Z~?@8Roeder, K. Bacanu, S. A. Sonpar, V. Zhang, X. Devlin, B.2005BAnalysis of single-locus tests to detect gene/disease associations207-19Genet Epidemiol283Computer Simulation Genetic Predisposition to Disease/*genetics Genotype Haplotypes/genetics Humans Linkage Disequilibrium/genetics Polymorphism, Single Nucleotide/*genetics Regression Analysis SoftwareAprA goal of association analysis is to determine whether variation in a particular candidate region or gene is associated with liability to complex disease. To evaluate such candidates, ubiquitous Single Nucleotide Polymorphisms (SNPs) are useful. It is critical, however, to select a set of SNPs that are in substantial linkage disequilibrium (LD) with all other polymorphisms in the region. Whether there is an ideal statistical framework to test such a set of 'tag SNPs' for association is unknown. Compared to tests for association based on frequencies of haplotypes, recent evidence suggests tests for association based on linear combinations of the tag SNPs (Hotelling T(2) test) are more powerful. Following this logical progression, we wondered if single-locus tests would prove generally more powerful than the regression-based tests? We answer this question by investigating four inferential procedures: the maximum of a series of test statistics corrected for multiple testing by the Bonferroni procedure, T(B), or by permutation of case-control status, T(P); a procedure that tests the maximum of a smoothed curve fitted to the series of of test statistics, T(S); and the Hotelling T(2) procedure, which we call T(R). These procedures are evaluated by simulating data like that from human populations, including realistic levels of LD and realistic effects of alleles conferring liability to disease. We find that power depends on the correlation structure of SNPs within a gene, the density of tag SNPs, and the placement of the liability allele. The clearest pattern emerges between power and the number of SNPs selected. When a large fraction of the SNPs within a gene are tested, and multiple SNPs are highly correlated with the liability allele, T(S) has better power. Using a SNP selection scheme that optimizes power but also requires a substantial number of SNPs to be genotyped (roughly 10-20 SNPs per gene), power of T(P) is generally superior to that for the other procedures, including T(R). Finally, when a SNP selection procedure that targets a minimal number of SNPs per gene is applied, the average performances of T(P) and T(R) are indistinguishable.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15637715 k0741-0395 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S.15637715hDepartment of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. roeder@stat.cmu.edu~?A1Roeder, K. Bacanu, S. A. Wasserman, L. Devlin, B.2006JUsing linkage genome scans to improve power of association in genome scans243-52Am J Hum Genet782FebScanning the genome for association between markers and complex diseases typically requires testing hundreds of thousands of genetic polymorphisms. Testing such a large number of hypotheses exacerbates the trade-off between power to detect meaningful associations and the chance of making false discoveries. Even before the full genome is scanned, investigators often favor certain regions on the basis of the results of prior investigations, such as previous linkage scans. The remaining regions of the genome are investigated simultaneously because genotyping is relatively inexpensive compared with the cost of recruiting participants for a genetic study and because prior evidence is rarely sufficient to rule out these regions as harboring genes with variation of conferring liability (liability genes). However, the multiple testing inherent in broad genomic searches diminishes power to detect association, even for genes falling in regions of the genome favored a priori. Multiple testing problems of this nature are well suited for application of the false-discovery rate (FDR) principle, which can improve power. To enhance power further, a new FDR approach is proposed that involves weighting the hypotheses on the basis of prior data. We present a method for using linkage data to weight the association P values. Our investigations reveal that if the linkage study is informative, the procedure improves power considerably. Remarkably, the loss in power is small, even when the linkage study is uninformative. For a class of genetic models, we calculate the sample size required to obtain useful prior information from a linkage study. This inquiry reveals that, among genetic models that are seemingly equal in genetic information, some are much more promising than others for this mode of analysis.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16400608 !0002-9297 (Print) Journal Article16400608_Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, USA. roeder@stat.cmu.edu. ~?BRohde, K. Fuerst, R.2001Haplotyping and estimation of haplotype frequencies for closely linked biallelic multilocus genetic phenotypes including nuclear family information289-95 Hum Mutat174Algorithms Alleles Chromosome Mapping/*methods Computer Simulation Female Gene Frequency/*genetics Haplotypes/*genetics Heterozygote Humans Likelihood Functions Linkage (Genetics)/*genetics Male Nuclear Family Phenotype SoftwareAprWith the discovery of single nucleotide polymorphisms (SNP) along the genome, genotyping of large samples of biallelic multilocus genetic phenotypes for (fine) mapping of disease genes or for population studies has become standard practice. A genetic trait, however, is mainly caused by an underlying defective haplotype, and populations are best characterized by their haplotype frequencies. Therefore, it is essential to infer from the phase-unknown genetic phenotypes in a sample drawn from a population the haplotype frequencies in the population and the underlying haplotype pairs in the sample in order to find disease predisposing genes by some association or haplotype sharing algorithm. Haplotype frequencies and haplotype pairs are estimated via a maximum likelihood approach by a well-known expectation maximization (EM) algorithm, adapting it to a large number (up to 30) of biallelic loci (SNP), and including nuclear family information, if available, into the analysis. Parents are treated as an independent sample from the population. Their genotyped offspring reduces the number of potential haplotype pairs for both parents, resulting in a higher accuracy of the estimation, and may also reduce computation time. In a series of simulations our approach of including nuclear family information has been tested against both the EM algorithm without nuclear family information and an alternative approach using GENEHUNTER for the haplotyping of the families, using the locus-by-locus allele counts of the sample. Our new approach is more precise in haplotyping in cases of a high number of heterozygous loci, whereas for a moderate number of heterozygous positions in the sample all three different approaches gave the same perfect results.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11295827 &1098-1004 (Electronic) Journal Article11295827RMax-Delbrueck-Centrum for Molecular Medicine, Berlin, Germany. rohde@mdc-berlin.de ~?CRommens, J. M. Iannuzzi, M. C. Kerem, B. Drumm, M. L. Melmer, G. Dean, M. Rozmahel, R. Cole, J. L. Kennedy, D. Hidaka, N. et al.,1989JIdentification of the cystic fibrosis gene: chromosome walking and jumping1059-65Science2454922Animals Base Sequence Cattle Chickens *Chromosome Mapping *Chromosomes, Human, Pair 7 Cloning, Molecular/methods Cricetinae Cystic Fibrosis/*genetics DNA Probes Genes, Overlapping *Genes, Recessive Genetic Markers Humans Mice Nucleic Acid Hybridization Restriction Mapping/methodsSep 8An understanding of the basic defect in the inherited disorder cystic fibrosis requires cloning of the cystic fibrosis gene and definition of its protein product. In the absence of direct functional information, chromosomal map position is a guide for locating the gene. Chromosome walking and jumping and complementary DNA hybridization were used to isolate DNA sequences, encompassing more than 500,000 base pairs, from the cystic fibrosis region on the long arm of human chromosome 7. Several transcribed sequences and conserved segments were identified in this cloned region. One of these corresponds to the cystic fibrosis gene and spans approximately 250,000 base pairs of genomic DNA.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2772657 g0036-8075 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.2772657MDepartment of Genetics, Hospital for Sick Children, Toronto, Ontario, Canada.b?D Rothman, K.J1986Modern Epidemiology Boston, MA.Little Brown and Companyt?ES.A.G.E,2006R v5.2 Statistical Analysis for Genetic Epidemiology. http://darwin.cwru.edu/sage/. ~?F)Saccone, S. F. Rice, J. P. Saccone, N. L.2006hPower-based, phase-informed selection of single nucleotide polymorphisms for disease association screens459-70Genet Epidemiol306UAlgorithms Chromosome Mapping Chromosomes, Human, Pair 7/genetics Computer Simulation European Continental Ancestry Group/*genetics Genetic Predisposition to Disease/*epidemiology Genome, Human Genotype Haplotypes Humans *Linkage (Genetics) Linkage Disequilibrium Models, Statistical *Polymorphism, Single Nucleotide Risk Factors Sample SizeSepJSingle nucleotide polymorphisms (SNPs) are becoming widely used as genotypic markers in genetic association studies of common, complex human diseases. For such association screens, a crucial part of study design is determining what SNPs to prioritize for genotyping. We present a novel power-based algorithm to select a subset of tag SNPs for genotyping from a map of available SNPs. Blocks of markers in strong linkage disequilibrium (LD) are identified, and SNPs are selected to represent each block such that power to detect disease association with an underlying disease allele in LD with block members is preserved; all markers outside of blocks are also included in the tagging subset. A key, novel element of this method is that it incorporates information about the phase of LD observed among marker pairs to retain markers likely to be in coupling phase with an underlying disease locus, thus increasing power compared to a phase-blind approach. Power calculations illustrate important issues regarding LD phase and make clear the advantages of our approach to SNP selection. We apply our algorithm to genotype data from the International HapMap Consortium and demonstrate that considerable reduction in SNP genotyping may be attained while retaining much of the available power for a disease association screen. We also demonstrate that these tag SNPs effectively represent underlying variants not included in the LD analysis and SNP selection, by using leave-one-out tests to show that most (approximately 90%) of the "untyped" variants lying in blocks are in coupling-phase LD with a tag SNP. Additional performance tests using the HapMap ENCyclopedia of DNA Elements (ENCODE) regions show that the method compares well with the popular r2 bin tagging method. This work is a concrete example of how empirical LD phase may be used to benefit study design.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16685721 F0741-0395 (Print) Journal Article Research Support, N.I.H., Extramural16685721PDepartment of Psychiatry, Washington University, St. Louis, Missouri 63110, USA.~?GSachidanandam, R. Weissman, D. Schmidt, S. C. Kakol, J. M. Stein, L. D. Marth, G. Sherry, S. Mullikin, J. C. Mortimore, B. J. Willey, D. L. Hunt, S. E. Cole, C. G. Coggill, P. C. Rice, C. M. Ning, Z. Rogers, J. Bentley, D. R. Kwok, P. Y. Mardis, E. R. Yeh, R. T. Schultz, B. Cook, L. Davenport, R. Dante, M. Fulton, L. Hillier, L. Waterston, R. H. McPherson, J. D. Gilman, B. Schaffner, S. Van Etten, W. J. Reich, D. Higgins, J. Daly, M. J. Blumenstiel, B. Baldwin, J. Stange-Thomann, N. Zody, M. C. Linton, L. Lander, E. S. Altshuler, D.2001`A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms928-33Nature4096822Chromosome Mapping Genetics, Medical Genetics, Population *Genome, Human Humans Nucleotides *Polymorphism, Single Nucleotide *Variation (Genetics)Feb 15We describe a map of 1.42 million single nucleotide polymorphisms (SNPs) distributed throughout the human genome, providing an average density on available sequence of one SNP every 1.9 kilobases. These SNPs were primarily discovered by two projects: The SNP Consortium and the analysis of clone overlaps by the International Human Genome Sequencing Consortium. The map integrates all publicly available SNPs with described genes and other genomic features. We estimate that 60,000 SNPs fall within exon (coding and untranslated regions), and 85% of exons are within 5 kb of the nearest SNP. Nucleotide diversity varies greatly across the genome, in a manner broadly consistent with a standard population genetic model of human history. This high-density SNP map provides a public resource for defining haplotype variation across the genome, and should help to identify biomedically important genes for diagnosis and therapy.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11237013 g0028-0836 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11237013(Cold Spring Harbor, New York 11724, USA.~?HiSaiki, R. K. Gelfand, D. H. Stoffel, S. Scharf, S. J. Higuchi, R. Horn, G. T. Mullis, K. B. Erlich, H. A.1988QPrimer-directed enzymatic amplification of DNA with a thermostable DNA polymerase487-91Science2394839 Cloning, Molecular DNA/*genetics DNA, Recombinant DNA-Directed DNA Polymerase/*metabolism Electrophoresis, Agar Gel Globins/genetics *Heat Humans *Nucleic Acid Amplification Techniques Nucleic Acid Denaturation Nucleic Acid Hybridization RNA/genetics Thermus/enzymologyJan 29A thermostable DNA polymerase was used in an in vitro DNA amplification procedure, the polymerase chain reaction. The enzyme, isolated from Thermus aquaticus, greatly simplifies the procedure and, by enabling the amplification reaction to be performed at higher temperatures, significantly improves the specificity, yield, sensitivity, and length of products that can be amplified. Single-copy genomic sequences were amplified by a factor of more than 10 million with very high specificity, and DNA segments up to 2000 base pairs were readily amplified. In addition, the method was used to amplify and detect a target DNA molecule present only once in a sample of 10(5) cells.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2448875 !0036-8075 (Print) Journal Article2448875FCetus Corporation, Department of Human Genetics, Emeryville, CA 94608.?ISaris,W.E. Satorra, A.1993/Power evaluations in structural equation models181-204#Testing Structural Equation Models K.A. Bollen J.S. LongNewbury Park, CASage~?JSatagopan, J. M. Elston, R. C.2003DOptimal two-stage genotyping in population-based association studies149-57Genet Epidemiol252o*Case-Control Studies Genetic Markers/genetics *Genotype Humans *Models, Genetic Monte Carlo Method Sample SizeSepWe propose a cost-effective two-stage approach to investigate gene-disease associations when testing a large number of candidate markers using a case-control design. Under this approach, all the markers are genotyped and tested at stage 1 using a subset of affected cases and unaffected controls, and the most promising markers are genotyped on the remaining individuals and tested using all the individuals at stage 2. The sample size at stage 1 is chosen such that the power to detect the true markers of association is 1-beta(1) at significance level alpha(1). The most promising markers are tested at significance level alpha(2) at stage 2. In contrast, a one-stage approach would evaluate and test all the markers on all the cases and controls to identify the markers significantly associated with the disease. The goal is to determine the two-stage parameters (alpha(1), beta(1), alpha(2)) that minimize the cost of the study such that the desired overall significance is alpha and the desired power is close to 1-beta, the power of the one-stage approach. We provide analytic formulae to estimate the two-stage parameters. The properties of the two-stage approach are evaluated under various parametric configurations and compared with those of the corresponding one-stage approach. The optimal two-stage procedure does not depend on the signal of the markers associated with the study. Further, when there is a large number of markers, the optimal procedure is not substantially influenced by the total number of markers associated with the disease. The results show that, compared to a one-stage approach, a two-stage procedure typically halves the cost of the study.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12916023 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.12916023Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA. satagopj@mskcc.org!~?K/Satagopan, J. M. Venkatraman, E. S. Begg, C. B.2004STwo-stage designs for gene-disease association studies with sample size constraints589-97 Biometrics603Biometry Case-Control Studies Genetic Diseases, Inborn/etiology/*genetics Genetic Markers Humans Linkage Disequilibrium Risk Factors Sample SizeSepGene-disease association studies based on case-control designs may often be used to identify candidate polymorphisms (markers) conferring disease risk. If a large number of markers are studied, genotyping all markers on all samples is inefficient in resource utilization. Here, we propose an alternative two-stage method to identify disease-susceptibility markers. In the first stage all markers are evaluated on a fraction of the available subjects. The most promising markers are then evaluated on the remaining individuals in Stage 2. This approach can be cost effective since markers unlikely to be associated with the disease can be eliminated in the first stage. Using simulations we show that, when the markers are independent and when they are correlated, the two-stage approach provides a substantial reduction in the total number of marker evaluations for a minimal loss of power. The power of the two-stage approach is evaluated when a single marker is associated with the disease, and in the presence of multiple disease-susceptibility markers. As a general guideline, the simulations over a wide range of parametric configurations indicate that evaluating all the markers on 50% of the individuals in Stage 1 and evaluating the most promising 10% of the markers on the remaining individuals in Stage 2 provides near-optimal power while resulting in a 45% decrease in the total number of marker evaluations.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15339280 F0006-341X (Print) Journal Article Research Support, U.S. Gov't, P.H.S.15339280Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA. satagopj@mskcc.org`~?LJSatagopan, J. M. Verbel, D. A. Venkatraman, E. S. Offit, K. E. Begg, C. B.20026Two-stage designs for gene-disease association studies163-70 Biometrics581Breast Neoplasms/*genetics Case-Control Studies Computer Simulation Female Genes, BRCA1 Genes, BRCA2 Genetic Screening/economics Germ-Line Mutation Humans *Models, Genetic Polymorphism, Single Nucleotide/genetics RiskMarxThe goal of this article is to describe a two-stage design that maximizes the power to detect gene-disease associations when the principal design constraint is the total cost, represented by the total number of gene evaluations rather than the total number of individuals. In the first stage, all genes of interest are evaluated on a subset of individuals. The most promising genes are then evaluated on additional subjects in the second stage. This will eliminate wastage of resources on genes unlikely to be associated with disease based on the results of the first stage. We consider the case where the genes are correlated and the case where the genes are independent. Using simulation results, it is shown that, as a general guideline when the genes are independent or when the correlation is small, utilizing 75% of the resources in stage 1 to screen all the markers and evaluating the most promising 10% of the markers with the remaining resources provides near-optimal power for a broad range of parametric configurations. This translates to screening all the markers on approximately one quarter of the required sample size in stage 1.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11890312 F0006-341X (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11890312Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA. satago@biosta.mskcc.org?MSatorra, A. Saris,W.E.1985GThe power of the likelihood ratio test in covariance structure analysis83-90Psychometrika 50~?N&Satten, G. A. Flanders, W. D. Yang, Q.2001Accounting for unmeasured population substructure in case-control studies of genetic association using a novel latent-class model466-77Am J Hum Genet682Alleles Case-Control Studies Gene Frequency Genetic Predisposition to Disease/*genetics Genetics, Population Humans Linkage Disequilibrium *Models, Genetic Tandem Repeat Sequences/geneticsFebWe propose a novel latent-class approach to detect and account for population stratification in a case-control study of association between a candidate gene and a disease. In our approach, population substructure is detected and accounted for using data on additional loci that are in linkage equilibrium within subpopulations but have alleles that vary in frequency between subpopulations. We have tested our approach using simulated data based on allele frequencies in 12 short tandem repeat (STR) loci in four populations in Argentina.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11170894 !0002-9297 (Print) Journal Article11170894SCenters for Disease Control and Prevention, Atlanta, GA 30341, USA. GSatten@cdc.gov ~?OWSawcer, S. Jones, H. B. Judge, D. Visser, F. Compston, A. Goodfellow, P. N. Clayton, D.1997PEmpirical genomewide significance levels established by whole genome simulations223-9Genet Epidemiol143Female *Genetic Screening Genome, Human Humans *Linkage (Genetics) Lod Score Male Microsatellite Repeats Monte Carlo Method Multiple Sclerosis/*genetics Pedigree Software Statistics, Nonparametric9The advent of high-resolution genetic maps and semiautomated genotyping technology has opened the way for genome screening in genetically complex traits. Many such screens are now under way, or completed, most using multipoint nonparametric linkage analysis of affected sibling pairs. This type of linkage analysis uses all the available genotype information to calculate the maximum lod score (MLS) value at each point in the genome, and thereby generates MLS profiles along each chromosome. Any positive MLS values indicate potential linkage, but the peaks in these profiles, which may be referred to as "hits," identify the most likely locations of disease susceptibility genes. However, such analysis presents serious problems of multiple testing, and the assessment of the statistical significance of hits has become a contentious issue [Lander and Kruglyak (1995) Nat Genet 11:241-247; Curtis (1996) Nat Genet 12:356-357; Witte et al. (1996) Nat Genet 12:355-356]. Having recently completed a genome screen in multiple sclerosis, we decided to investigate the statistical properties of our study by simulation. We report here in detail the results of this simulation study. Our main conclusion is that, for the particular set of families and markers used in our screen, an MLS of 3.2 carries a genome-wide significance of 5% (that is, there is a 5% probability of observing at least one false hit, above this threshold in a complete genome screen). This value is closer to the familiar limit of 3.0, originally suggested by Morton [1955; Am J Hum Genet 7:277-318] than to the more stringent limit of 4.0 recently proposed by Lander and Kruglyak [1995; Nat Genet 11:241-247]. This is somewhat reassuring, in view of the very large sample sizes that would be necessary to achieve adequate power to detect linkage at the more stringent threshold.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9181352 B0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't9181352OUniversity of Cambridge Neurology Unit, Addenbrooke's Hospital, United Kingdom. z~?PSaxena, R. de Bakker, P. I. Singer, K. Mootha, V. Burtt, N. Hirschhorn, J. N. Gaudet, D. Isomaa, B. Daly, M. J. Groop, L. Ardlie, K. G. Altshuler, D.2006\Comprehensive association testing of common mitochondrial DNA variation in metabolic disease54-61Am J Hum Genet791Body Mass Index Case-Control Studies DNA, Mitochondrial/*genetics Diabetes Mellitus, Type 2/*genetics Humans Metabolic Diseases/*genetics Polymorphism, Single NucleotideJulMany lines of evidence implicate mitochondria in phenotypic variation: (a) rare mutations in mitochondrial proteins cause metabolic, neurological, and muscular disorders; (b) alterations in oxidative phosphorylation are characteristic of type 2 diabetes, Parkinson disease, Huntington disease, and other diseases; and (c) common missense variants in the mitochondrial genome (mtDNA) have been implicated as having been subject to natural selection for adaptation to cold climates and contributing to "energy deficiency" diseases today. To test the hypothesis that common mtDNA variation influences human physiology and disease, we identified all 144 variants with frequency >1% in Europeans from >900 publicly available European mtDNA sequences and selected 64 tagging single-nucleotide polymorphisms that efficiently capture all common variation (except the hypervariable D-loop). Next, we evaluated the complete set of common mtDNA variants for association with type 2 diabetes in a sample of 3,304 diabetics and 3,304 matched nondiabetic individuals. Association of mtDNA variants with other metabolic traits (body mass index, measures of insulin secretion and action, blood pressure, and cholesterol) was also tested in subsets of this sample. We did not find a significant association of common mtDNA variants with these metabolic phenotypes. Moreover, we failed to identify any physiological effect of alleles that were previously proposed to have been adaptive for energy metabolism in human evolution. More generally, this comprehensive association-testing framework can readily be applied to other diseases for which mitochondrial dysfunction has been implicated.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16773565 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't16773565VCenter for Human Genetic Research, Massachusetts General Hospital, Boston, 02114, USA.k?Q Schafer, J.L.1997(Analysis of Incomplete Multivariate DataLondonChapman and Hallf?R Schafer, J.L.1999Multiple imputation: a primer3-15Stat. Methods Med. Res8N~?SSchafer, J. L. Graham, J. W.2002.Missing data: our view of the state of the art147-77Psychol Methods72B*Databases Humans Models, Theoretical Research/*standards SoftwareJun;Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12090408 F1082-989X (Print) Journal Article Research Support, U.S. Gov't, P.H.S.12090408Department of Statistics and the Methodology Center, Pennsylvania State University, University Park 16802, USA. jls@stat.psu.edu~?THSchaffner, S. F. Foo, C. Gabriel, S. Reich, D. Daly, M. J. Altshuler, D.2005FCalibrating a coalescent simulation of human genome sequence variation1576-83 Genome Res1511Computer Simulation *Evolution, Molecular Gene Frequency *Genetics, Population Genome, Human/*genetics Genomics/*methods Humans Linkage Disequilibrium *Models, Genetic *Variation (Genetics)NovPopulation genetic models play an important role in human genetic research, connecting empirical observations about sequence variation with hypotheses about underlying historical and biological causes. More specifically, models are used to compare empirical measures of sequence variation, linkage disequilibrium (LD), and selection to expectations under a "null" distribution. In the absence of detailed information about human demographic history, and about variation in mutation and recombination rates, simulations have of necessity used arbitrary models, usually simple ones. With the advent of large empirical data sets, it is now possible to calibrate population genetic models with genome-wide data, permitting for the first time the generation of data that are consistent with empirical data across a wide range of characteristics. We present here the first such calibrated model and show that, while still arbitrary, it successfully generates simulated data (for three populations) that closely resemble empirical data in allele frequency, linkage disequilibrium, and population differentiation. No assertion is made about the accuracy of the proposed historical and recombination model, but its ability to generate realistic data meets a long-standing need among geneticists. We anticipate that this model, for which software is publicly available, and others like it will have numerous applications in empirical studies of human genetics.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16251467 T1088-9051 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't16251467wProgram in Medical and Population Genetics, The Broad Institute, Cambridge, Massachusetts 02139, USA. sfs@broad.mit.edu ~?UGSchaid, D. J. Rowland, C. M. Tines, D. E. Jacobson, R. M. Poland, G. A.2002YScore tests for association between traits and haplotypes when linkage phase is ambiguous425-34Am J Hum Genet702vAlgorithms Case-Control Studies Chi-Square Distribution Chromosome Mapping/*methods/*statistics & numerical data Computer Simulation Genetic Predisposition to Disease/genetics HLA Antigens/genetics/immunology Haplotypes/*genetics Humans Linkage (Genetics)/*genetics Measles Vaccine/immunology Quantitative Trait, Heritable Reproducibility of Results Research Design SoftwareFebA key step toward the discovery of a gene related to a trait is the finding of an association between the trait and one or more haplotypes. Haplotype analyses can also provide critical information regarding the function of a gene; however, when unrelated subjects are sampled, haplotypes are often ambiguous because of unknown linkage phase of the measured sites along a chromosome. A popular method of accounting for this ambiguity in case-control studies uses a likelihood that depends on haplotype frequencies, so that the haplotype frequencies can be compared between the cases and controls; however, this traditional method is limited to a binary trait (case vs. control), and it does not provide a method of testing the statistical significance of specific haplotypes. To address these limitations, we developed new methods of testing the statistical association between haplotypes and a wide variety of traits, including binary, ordinal, and quantitative traits. Our methods allow adjustment for nongenetic covariates, which may be critical when analyzing genetically complex traits. Furthermore, our methods provide several different global tests for association, as well as haplotype-specific tests, which give a meaningful advantage in attempts to understand the roles of many different haplotypes. The statistics can be computed rapidly, making it feasible to evaluate the associations between many haplotypes and a trait. To illustrate the use of our new methods, they are applied to a study of the association of haplotypes (composed of genes from the human-leukocyte-antigen complex) with humoral immune response to measles vaccination. Limited simulations are also presented to demonstrate the validity of our methods, as well as to provide guidelines on how our methods could be used.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11791212 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11791212iDepartment of Health Sciences Research, Mayo Clinic/Foundation, Rochester, MN 55905, USA. schaid@mayo.eduR~?VSchaid, D. J. Sommer, S. S.1994WComparison of statistics for candidate-gene association studies using cases and parents402-9Am J Hum Genet552Alleles *Case-Control Studies Chi-Square Distribution Confounding Factors (Epidemiology) Ethnic Groups/genetics Gene Frequency Genes, Dominant Genes, Recessive Genetic Diseases, Inborn/*genetics *Genotype Heterozygote Homozygote Humans Likelihood Functions Matched-Pair Analysis *Models, Genetic Odds Ratio *Parents Regression Analysis Reproducibility of Results Research Design RiskAugStudies of association between candidate genes and disease can be designed to use cases with disease, and in place of nonrelated controls, their parents. The advantage of this design is the elimination of spurious differences due to ethnic differences between cases and nonrelated controls. However, several statistical methods of analysis have been proposed in the literature, and the choice of analysis is not always clear. We review some of the statistical methods currently developed and present two new statistical methods aimed at specific genetic hypotheses of dominance and recessivity of the candidate gene. These new methods can be more powerful than other current methods, as demonstrated by simulations. The basis of these new statistical methods is a likelihood approach. The advantage of the likelihood framework is that regression models can be developed to assess genotype-environment interactions, as well as the relative contribution that alleles at the candidate-gene locus make to the relative risk (RR) of disease. This latter development allows testing of (1) whether interactions between alleles exist, on the scale of log RR, and (2) whether alleles originating from the mother or father of a case impart different risks, i.e., genomic imprinting.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8037216 X0002-9297 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.8037216TDepartment of Health Sciences Research, Mayo Clinic/Foundation, Rochester, MN 55905.?WSchlesselman, J.J19820Case-Control Studies: Design, Conduct, Analysis. New York, NY.Oxford University PressM~?X'Schmitz, S. Cherny, S. S. Fulker, D. W.1998/Increase in power through multivariate analyses357-63 Behav Genet285Genotype Humans Longitudinal Studies *Models, Genetic Multivariate Analysis *Phenotype Probability Sample Size Social Environment Twin StudiesSepPower to detect genetic and environmental influences increases not only with sample size but also with the number of measurements through longitudinal and/or multivariate designs, if those measurements correlate with each other. Power simulations are presented for uni- through quadrivariate cases, with differing genetic and environmental parameters. Even though subject attrition is a problem for most longitudinal studies, the gain in power available may more than make up for this shortcoming in many situations. In terms of planning studies to examine genetic and environmental influences, power calculations should not only consider sample size but number of measurements on particular phenotypes and their intercorrelations.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9926617 g0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.9926617kDepartment of Psychiatry, University of Colorado Health Sciences Center, Denver, USA. schmitzs@colorado.edu ~?Y Schork, N. J.1993}Extended multipoint identity-by-descent analysis of human quantitative traits: efficiency, power, and modeling considerations1306-19Am J Hum Genet536Genetic Markers Genotype Humans Likelihood Functions Mathematics *Models, Genetic Nuclear Family Phenotype *Variation (Genetics)DecGoldgar introduced a novel marker-based method for partitioning the variation of a quantitative trait into specific chromosomal regions. Unlike traditional linkage mapping methods, Goldgar's method does not require the estimation of statistical quantities characterizing each locus thought to influence the trait under scrutiny (e.g., allele frequencies, penetrances, etc.). Goldgar's method is thus more flexible and less model dependent than many traditional marker-based genetic analysis techniques. Unfortunately, however, many of the properties of Goldgar's method have not been investigated. In this paper, the utility of an extended version of Goldgar's approach is studied in settings in which sibships are taken as the sampling unit of interest. The extensions discussed resolve around the incorporation of a wider variety of effects and factors into Goldgar's basic model. Analytic studies pertaining to power, sample-size requirements, and estimation procedures for the proposed extended version of Goldgar's method are described. Hypothesis-testing strategies are also discussed. The results of the analytic studies indicate that, although an extended sib-pair version of Goldgar's variance-partitioning approach to modeling the chromosomal determinants of a quantitative trait will be useful only for traits with high heritabilities or when fine-scale genetic maps can be employed. Goldgar's technique as a whole has promise, as it can be made relatively robust statistically, refined through some simple and intuitive extensions, and can be easily adapted to work with more complex sampling units. Further extensions of Goldgar's methods are proposed, and areas in need of additional research are discussed.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8250047 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8250047EDepartment of Medicine, University of Michigan, Ann Arbor 48109-0500.)~?Z&Schork, N. J. Allison, D. B. Thiel, B.19960Mixture distributions in human genetics research155-78Stat Methods Med Res52Genetic Heterogeneity Genetic Techniques/*statistics & numerical data *Genetics, Population Humans Likelihood Functions Linkage (Genetics) *Models, StatisticalJunThe use of mixture distributions in genetics research dates back to at least the late 1800s when Karl Pearson applied them in an analysis of crab morphometry. Pearson's use of normal mixture distributions to model the mixing of different species of crab (or 'families' of crab as he referred to them) within a defined geographic area motivated further use of mixture distributions in genetics research settings, and ultimately led to their development and recognition as intuitive modelling devices for the effects of underlying genes on quantitative phenotypic (i.e. trait) expression. In addition, mixture distributions are now used routinely to model or accommodate the genetic heterogeneity thought to underlie many human diseases. Specific applications of mixture distribution models in contemporary human genetics research are, in fact, too numerous to count. Despite this long, consistent and arguably illustrious history of use, little mention of mixture distributions in genetics research is made in many recent reviews on mixture models. This review attempts to rectify this by providing insight into the role that mixture distributions play in contemporary human genetics research. Tables providing examples from the literature that describe applications of mixture models in human genetics research are offered as a way of acquainting the interested reader with relevant studies. In addition, some of the more problematic aspects of the use of mixture models in genetics research are outlined and addressed.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8817796 (0962-2802 (Print) Journal Article Review8817796YDepartment of Genetics, Case Western Reserve University, Cleveland, Ohio 44109-1998, USA. ~?[Schuit, S. C. van Meurs, J. B. Bergink, A. P. van der Klift, M. Fang, Y. Leusink, G. Hofman, A. van Leeuwen, J. P. Uitterlinden, A. G. Pols, H. A.2004cHeight in pre- and postmenopausal women is influenced by estrogen receptor alpha gene polymorphisms303-9J Clin Endocrinol Metab891Age Factors Aged Aged, 80 and over Alleles Body Height/*genetics Body Mass Index Bone Density Deoxyribonucleases, Type II Site-Specific Estrogen Receptor alpha Female Haplotypes Humans Linkage Disequilibrium Lumbar Vertebrae Male Menarche Middle Aged Minisatellite Repeats/genetics Osteoporosis, Postmenopausal *Polymorphism, Genetic Polymorphism, Restriction Fragment Length *Postmenopause *Premenopause Receptors, Estrogen/*geneticsJanThe estrogen receptor alpha gene (ESR1) is known to be involved in metabolic pathways influencing growth. We have performed two population-based association studies using three common polymorphisms within this candidate gene to determine whether these are associated with variation in adult stature. In 607 women, aged 55-80 yr, from the Rotterdam Study, the ESR1 PvuII-XbaI haplotype 1 (px) and the L allele of the TA repeat polymorphism (<18 TA repeats) were significantly associated with an allele dose-dependent decrease in height. The per allele copy of ESR1 PvuII-XbaI haplotype 1 height was 0.9 cm shorter (P trend = 0.02) and 1.0 cm/allele copy of the TA repeat L allele (P trend = 0.003). These results were independent of age, age at menarche and menopause, and lumbar spine bone mineral density and remained significant after participants with vertebral fractures were excluded. In 483 men from the Rotterdam Study we found no association with height. In 1500 pre- and perimenopausal women from the Eindhoven Study a similar association was observed; women were 0.5 cm shorter per allele copy of the ESR1 haplotype 1 (P for trend = 0.03). In conclusion, we demonstrate a role for genetic variations in the estrogen receptor alpha gene in determining adult stature in women.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14715865 B0021-972X (Print) Journal Article Research Support, Non-U.S. Gov't14715865\Department of Internal Medicine, Erasmus Medical Center, 3000 DR Rotterdam, The Netherlands. ~?\DSchulze, T. G. Buervenich, S. Badner, J. A. Steele, C. J. Detera-Wadleigh, S. D. Dick, D. Foroud, T. Cox, N. J. MacKinnon, D. F. Potash, J. B. Berrettini, W. H. Byerley, W. Coryell, W. DePaulo, J. R., Jr. Gershon, E. S. Kelsoe, J. R. McInnis, M. G. Murphy, D. L. Reich, T. Scheftner, W. Nurnberger, J. I., Jr. McMahon, F. J.2004Loci on chromosomes 6q and 6p interact to increase susceptibility to bipolar affective disorder in the national institute of mental health genetics initiative pedigrees18-23Biol Psychiatry561-Bipolar Disorder/epidemiology/*genetics *Chromosomes, Human, Pair 6 Epistasis, Genetic Female *Genetic Predisposition to Disease Humans *Linkage (Genetics) Lod Score Male National Institute of Mental Health (U.S.) Pedigree Psychotic Disorders/genetics Schizophrenia/genetics United States/epidemiologyJul 1BACKGROUND: We have reported genetic linkage between bipolar disorder and markers on chromosome 6q16.3-22.1 in the National Institute of Mental Health Genetics Initiative wave 3 pedigrees. Here we test for: 1) robustness of the linkage to differing analysis methods, genotyping error, and gender-specific maps; 2) parent-of-origin effects; and 3) interaction with markers within the schizophrenia linkage region on chromosome 6p. METHODS: Members of 245 families ascertained through a sibling pair affected with bipolar I or schizoaffective-bipolar disorder were genotyped with 18 markers spanning chromosome 6. Nonparametric linkage analysis was performed. RESULTS: Linkage to 6q is robust to analysis method, gender-specific map differences, and genotyping error. The locus confers a 1.4-fold increased risk. Affected siblings share the maternal more often than the paternal chromosome (p =.006), which could reflect a maternal parent-of-origin effect. There is a positive correlation between family-specific linkage scores on 6q and those on 6p22.2 (r =.26; p <.0001). Linkage analysis for each locus conditioned on evidence of linkage to the other increases the evidence for linkage at both loci (p <.0005). Logarithm of the odds (LOD) scores increased from 2.26 to 5.42 on 6q and from.35 to 2.26 on 6p22.2. CONCLUSIONS: These results support linkage of bipolar disorder to 6q, uncover a maternal parent-of-origin effect, and demonstrate an interaction of this locus with one on chromosome 6p22.2, previously linked only to schizophrenia.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15219468 y0006-3223 (Print) Journal Article Multicenter Study Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15219468lGenetic Basis of Mood and Anxiety Disorders, Mood and Anxiety Program, Bethesda, USA. schulze@zi-mannheim.de ~?]Schymick, J. C. Scholz, S. W. Fung, H. C. Britton, A. Arepalli, S. Gibbs, J. R. Lombardo, F. Matarin, M. Kasperaviciute, D. Hernandez, D. G. Crews, C. Bruijn, L. Rothstein, J. Mora, G. Restagno, G. Chio, A. Singleton, A. Hardy, J. Traynor, B. J.2007Genome-wide genotyping in amyotrophic lateral sclerosis and neurologically normal controls: first stage analysis and public release of data322-8 Lancet Neurol64Adult Aged Aged, 80 and over Amyotrophic Lateral Sclerosis/*genetics DNA Mutational Analysis/*methods Databases, Genetic/standards/trends Female Genetic Markers/genetics Genetic Predisposition to Disease/genetics Genetic Screening/*methods Genomic Library *Genotype Humans Male Middle Aged Molecular Biology/*methods/trends Mutation/genetics Polymorphism, Single Nucleotide/genetics Public Sector/*standards Reference ValuesAprBACKGROUND: The cause of sporadic ALS is currently unknown. Despite evidence for a role for genetics, no common genetic variants have been unequivocally linked to sporadic ALS. We sought to identify genetic variants associated with an increased or decreased risk for developing ALS in a cohort of American sporadic cases. METHODS: We undertook a genome-wide association study using publicly available samples from 276 patients with sporadic ALS and 271 neurologically normal controls. 555 352 unique SNPs were assayed in each sample using the Illumina Infinium II HumanHap550 SNP chip. FINDINGS: More than 300 million genotypes were produced in 547 participants. These raw genotype data are freely available on the internet and represent the first publicly accessible SNP data for ALS cases. 34 SNPs with a p value less than 0.0001 (two degrees of freedom) were found, although none of these reached significance after Bonferroni correction. INTERPRETATION: We generated publicly available genotype data for sporadic ALS patients and controls. No single locus was definitively associated with increased risk of developing disease, although potentially associated candidate SNPs were identified.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17362836 1474-4422 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, N.I.H., Intramural Research Support, Non-U.S. Gov't17362836Laboratory of Neurogenetics, National Institute on Aging, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.~?^QSebastiani, P. Lazarus, R. Weiss, S. T. Kunkel, L. M. Kohane, I. S. Ramoni, M. F.2003Minimal haplotype tagging9900-5Proc Natl Acad Sci U S A10017African Continental Ancestry Group/genetics Algorithms European Continental Ancestry Group/genetics Evolution, Molecular Female Haplotypes/*genetics Humans Male Models, Genetic *Polymorphism, Single Nucleotide Variation (Genetics)Aug 19The high frequency of single-nucleotide polymorphisms (SNPs) in the human genome presents an unparalleled opportunity to track down the genetic basis of common diseases. At the same time, the sheer number of SNPs also makes unfeasible genome-wide disease association studies. The haplotypic nature of the human genome, however, lends itself to the selection of a parsimonious set of SNPs, called haplotype tagging SNPs (htSNPs), able to distinguish the haplotypic variations in a population. Current approaches rely on statistical analysis of transmission rates to identify htSNPs. In contrast to these approximate methods, this contribution describes an exact, analytical, and lossless method, called BEST (Best Enumeration of SNP Tags), able to identify the minimum set of SNPs tagging an arbitrary set of haplotypes from either pedigree or independent samples. Our results confirm that a small proportion of SNPs is sufficient to capture the haplotypic variations in a population and that this proportion decreases exponentially as the haplotype length increases. We used BEST to tag the haplotypes of 105 genes in an African-American and a European-American sample. An interesting finding of this analysis is that the vast majority (95%) of the htSNPs in the European-American sample is a subset of the htSNPs of the African-American sample. This result seems to provide further evidence that a severe bottleneck occurred during the founding of Europe and the conjectured "Out of Africa" event.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12900503 0027-8424 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.12900503^Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.~?_Sebat, J. Lakshmi, B. Malhotra, D. Troge, J. Lese-Martin, C. Walsh, T. Yamrom, B. Yoon, S. Krasnitz, A. Kendall, J. Leotta, A. Pai, D. Zhang, R. Lee, Y. H. Hicks, J. Spence, S. J. Lee, A. T. Puura, K. Lehtimaki, T. Ledbetter, D. Gregersen, P. K. Bregman, J. Sutcliffe, J. S. Jobanputra, V. Chung, W. Warburton, D. King, M. C. Skuse, D. Geschwind, D. H. Gilliam, T. C. Ye, K. Wigler, M.2007?Strong association of de novo copy number mutations with autism445-9Science3165823Asperger Syndrome/genetics Autistic Disorder/*genetics Case-Control Studies Child Cytogenetic Analysis Female Gene Deletion *Gene Dosage Gene Duplication Genetic Predisposition to Disease *Genome, Human Germ-Line Mutation Humans In Situ Hybridization, Fluorescence Male Markov Chains Microsatellite Repeats *Mutation Nucleic Acid Hybridization Oligonucleotide Array Sequence Analysis Parents SiblingsApr 20We tested the hypothesis that de novo copy number variation (CNV) is associated with autism spectrum disorders (ASDs). We performed comparative genomic hybridization (CGH) on the genomic DNA of patients and unaffected subjects to detect copy number variants not present in their respective parents. Candidate genomic regions were validated by higher-resolution CGH, paternity testing, cytogenetics, fluorescence in situ hybridization, and microsatellite genotyping. Confirmed de novo CNVs were significantly associated with autism (P = 0.0005). Such CNVs were identified in 12 out of 118 (10%) of patients with sporadic autism, in 2 out of 77 (3%) of patients with an affected first-degree relative, and in 2 out of 196 (1%) of controls. Most de novo CNVs were smaller than microscopic resolution. Affected genomic regions were highly heterogeneous and included mutations of single genes. These findings establish de novo germline mutation as a more significant risk factor for ASD than previously recognized.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17363630 l1095-9203 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't17363630aCold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA. sebat@cshl.eduT~?`Sebat, J. Lakshmi, B. Troge, J. Alexander, J. Young, J. Lundin, P. Maner, S. Massa, H. Walker, M. Chi, M. Navin, N. Lucito, R. Healy, J. Hicks, J. Ye, K. Reiner, A. Gilliam, T. C. Trask, B. Patterson, N. Zetterberg, A. Wigler, M.20048Large-scale copy number polymorphism in the human genome525-8Science3055683Alleles Bacterial Proteins/metabolism Cell Line, Transformed Chromosome Aberrations Chromosome Mapping Chromosomes, Human/genetics Deoxyribonuclease HindIII/metabolism Deoxyribonucleases, Type II Site-Specific/metabolism Female Gene Deletion *Gene Dosage Gene Duplication Gene Frequency *Genome, Human Humans Male Markov Chains Oligonucleotide Array Sequence Analysis *Polymorphism, Genetic *Variation (Genetics)Jul 23The extent to which large duplications and deletions contribute to human genetic variation and diversity is unknown. Here, we show that large-scale copy number polymorphisms (CNPs) (about 100 kilobases and greater) contribute substantially to genomic variation between normal humans. Representational oligonucleotide microarray analysis of 20 individuals revealed a total of 221 copy number differences representing 76 unique CNPs. On average, individuals differed by 11 CNPs, and the average length of a CNP interval was 465 kilobases. We observed copy number variation of 70 different genes within CNP intervals, including genes involved in neurological function, regulation of cell growth, regulation of metabolism, and several genes known to be associated with disease.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15273396 1095-9203 (Electronic) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.15273396ACold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.D~?aySeldin, M. F. Shigeta, R. Villoslada, P. Selmi, C. Tuomilehto, J. Silva, G. Belmont, J. W. Klareskog, L. Gregersen, P. K.2006QEuropean population substructure: clustering of northern and southern populationse143 PLoS Genet29Arthritis, Rheumatoid/genetics Cluster Analysis Europe/ethnology European Continental Ancestry Group/*genetics Gene Frequency Genetic Markers *Genetics, Population Humans Jews/genetics Lactase/deficiency New York Polymorphism, Single Nucleotide/genetics Reproducibility of ResultsSep 15Using a genome-wide single nucleotide polymorphism (SNP) panel, we observed population structure in a diverse group of Europeans and European Americans. Under a variety of conditions and tests, there is a consistent and reproducible distinction between "northern" and "southern" European population groups: most individual participants with southern European ancestry (Italian, Spanish, Portuguese, and Greek) have >85% membership in the "southern" population; and most northern, western, eastern, and central Europeans have >90% in the "northern" population group. Ashkenazi Jewish as well as Sephardic Jewish origin also showed >85% membership in the "southern" population, consistent with a later Mediterranean origin of these ethnic groups. Based on this work, we have developed a core set of informative SNP markers that can control for this partition in European population structure in a variety of clinical and genetic studies.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17044734 K1553-7404 (Electronic) Journal Article Research Support, N.I.H., Extramural17044734Rowe Program in Human Genetics, Departments of Biological Chemistry and Medicine, University of California Davis, Davis, California, United States of America. mfseldin@ucdavis.edu?bSelf, S.G. Liang, K.Y1987oAsymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions.605-610J. Am. Stat. Assoc82R?c Sham, P.C.1998Statistics in Human GeneticsLondonArnold~?d3Sham, P. C. Cherny, S. S. Purcell, S. Hewitt, J. K.2000{Power of linkage versus association analysis of quantitative traits, by use of variance-components models, for sibship data1616-30Am J Hum Genet665nChi-Square Distribution Chromosome Mapping/*methods/statistics & numerical data Computer Simulation Gene Frequency/genetics Genetic Markers/genetics Genotype Haplotypes/genetics Humans Likelihood Functions Linkage Disequilibrium Lod Score Matched-Pair Analysis *Models, Genetic *Nuclear Family *Quantitative Trait, Heritable Sample Size Variation (Genetics)/geneticsMayOptimal design of quantitative-trait loci (QTL) mapping studies requires a precise understanding of the power of QTL linkage versus QTL association analysis, under a range of different conditions. In this article, we investigate the power of QTL linkage and association analyses for simple random sibship samples, under the variance-components model proposed by Fulker et al. After a brief description of an extension of this variance-components model, we show that the powers of both linkage and association analyses are crucially dependent on the proportion of phenotypic variance attributable to the QTL. The main difference between the two tests is that, whereas the power of association is directly related to the QTL heritability, the power of linkage is related more closely to the square of the QTL heritability. We also describe both how the power of linkage is attenuated by incomplete linkage and incomplete marker information and how the power of association is attenuated by incomplete linkage disequilibrium.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10762547 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.10762547Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, London SE5 8AF, United Kingdom. spjupcs@iop.kcl.ac.uk.l~?eSham, P. C. Purcell, S.2001YEquivalence between Haseman-Elston and variance-components linkage analyses for sib pairs1527-32Am J Hum Genet686Chi-Square Distribution Chromosome Mapping/*methods/*statistics & numerical data Computer Simulation Genotype Humans Linkage (Genetics)/*genetics Matched-Pair Analysis Nuclear Family Quantitative Trait, Heritable Regression AnalysisJunsThe Haseman-Elston regression method offers a simpler alternative to variance-components (VC) models, for the linkage analysis of quantitative traits. However, even the "revisited" method, which uses the cross-product--rather than the squared difference--in sib trait values, is, in general, less powerful than VC models. In this report, we clarify the relative efficiencies of existing Haseman-Elston methods and show how a new Haseman-Elston method can be constructed to have power equivalent to that of VC models. This method uses as the dependent variable a linear combination of squared sums and squared differences, in which the weights are determined by the overall trait correlation between sibs in a population. We show how this method can be used for both the selection of maximally informative sib pairs for genotyping and the subsequent analysis of such selected samples.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11353401 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11353401xSocial, Genetic & Developmental Research Centre, Institute of Psychiatry, London, SE5 8AF, England. p.sham@iop.kcl.ac.uk~?f5Sham, P. C. Purcell, S. Cherny, S. S. Abecasis, G. R.2002RPowerful regression-based quantitative-trait linkage analysis of general pedigrees238-53Am J Hum Genet712Chromosome Mapping/*statistics & numerical data Computer Simulation Female Humans Male *Pedigree *Quantitative Trait, Heritable Regression Analysis *Software Design Statistics, NonparametricAugWe present a new method of quantitative-trait linkage analysis that combines the simplicity and robustness of regression-based methods and the generality and greater power of variance-components models. The new method is based on a regression of estimated identity-by-descent (IBD) sharing between relative pairs on the squared sums and squared differences of trait values of the relative pairs. The method is applicable to pedigrees of arbitrary structure and to pedigrees selected on the basis of trait value, provided that population parameters of the trait distribution can be correctly specified. Ambiguous IBD sharing (due to incomplete marker information) can be accommodated in the method by appropriate specification of the variance-covariance matrix of IBD sharing between relative pairs. We have implemented this regression-based method and have performed simulation studies to assess, under a range of conditions, estimation accuracy, type I error rate, and power. For normally distributed traits and in large samples, the method is found to give the correct type I error rate and an unbiased estimate of the proportion of trait variance accounted for by the additive effects of the locus-although, in cases where asymptotic theory is doubtful, significance levels should be checked by simulations. In large sibships, the new method is slightly more powerful than variance-components models. The proposed method provides a practical and powerful tool for the linkage analysis of quantitative traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12111667 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.12111667SGDP Research Centre, Institute of Psychiatry, King's College, Denmark Hill, London SE5 8AF, United Kingdom. p.sham@iop.kcl.ac.uk~?g3Sham, P. C. Zhao, J. H. Cherny, S. S. Hewitt, J. K.2000iVariance-Components QTL linkage analysis of selected and non-normal samples: conditioning on trait valuesS22-8Genet Epidemiol 19 Suppl 1\*Analysis of Variance Computer Simulation *Linkage (Genetics) *Quantitative Trait, Heritable!Standard variance-components quantitative trait loci (QTL) linkage analysis can produce an elevated rate of type 1 errors when applied to selected samples and non-normal data. Here we describe an adjustment of the log-likelihood function based on conditioning on trait values. This leads to a likelihood ratio test that is valid in selected samples and non-normal data, and equal in power to alternative methods for analyzing selected samples that require knowledge of the ascertainment procedure or the trait values of non-selected individuals.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11055366 g0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11055366Social, Genetic and Developmental Psychiatry Research Center and Department of Psychiatry, Institute of Psychiatry, Denmark Hill, London, United Kingdom. p.sham@iop.kcl.ac.uk?hShaw, F.H. Geyer, C.J1997B Estimation and testing in constrained covariance component models95-102 Biometrika 84@~?iShete, S. Amos, C. I.2002oTesting for genetic linkage in families by a variance-components approach in the presence of genomic imprinting751-7Am J Hum Genet703Alleles Chromosome Mapping/*methods/statistics & numerical data Female Genome, Human Genomic Imprinting/*genetics Genotype Humans Lod Score Male Polymorphism, Genetic/genetics Probability Quantitative Trait, Heritable Recombination, Genetic/genetics Sample SizeMar2Some genes that affect development and behavior in mammals are known to be imprinted; and > or = 1% of all mammalian genes are imprinted. Hence, incorporating an imprinting parameter into linkage analysis may increase the power to detect linkage for these traits. Here we propose theoretical justifications for a recently developed model for testing of linkage, in the presence of genetic imprinting, between a quantitative-trait locus and a polymorphic marker; this is achieved in the variance-components framework. We also incorporate sex-specific recombination fractions into this model. We discuss the effects that imprinting and nonimprinting have on the power of the usual variance-components method and on the variance-components method that incorporates an imprinting parameter. We provide noncentrality parameters that can be used to determine the sample size necessary to attain a specified power for a given significance level, which is useful in the planning of a linkage study. Optimal strategies for a genome scan of potentially imprinted traits are discussed.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11836650 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11836650Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA. sshete@mdanderson.org~?jShete, S. Zhou, X.2005[Parametric approach to genomic imprinting analysis with applications to Angelman's syndrome26-33 Hum Hered591Angelman Syndrome/*genetics Computer Simulation Female Genomic Imprinting/*genetics Humans Linkage (Genetics)/*genetics Lod Score Male Models, Genetic Pedigree SoftwareGenomic imprinting is a mechanism by which only one copy of a gene pair is expressed, and this expression is determined by the parental origin of the copy. The deregulation of imprinted genes has been implicated in a number of human diseases. The Imprinted Gene Catalogue now has more than 200 genes listed, and estimates based on mouse models suggest many more may exist in humans. Therefore, the development of methods to identify such genes is important. In this communication, we present a parametric model-based approach to analyzing arbitrary-sized pedigree data for genomic imprinting. We have modified widely used LINKAGE program to incorporate our proposed approach. In addition, our approach allows for the use of sex-specific recombinations in the analysis, which is of particular importance in a genome-wide analysis for imprinted genes. We compared our imprinting analysis approach to that implemented in the GENEHUNTER-IMPRINT program using simulation studies as well as by analyzing causal genes in Angelman's syndrome families, which are known to be imprinted. These analyses showed that the proposed approach is very powerful for detecting imprinted genes in large pedigrees.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15802919 !0001-5652 (Print) Journal Article15802919~Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA. sshete@mdanderson.org0~?k4Skol, A. D. Scott, L. J. Abecasis, G. R. Boehnke, M.2006nJoint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies209-13 Nat Genet382Alleles Case-Control Studies DNA Replication/*genetics Gene Frequency/genetics Genetic Heterogeneity Genetic Markers/genetics Genetic Predisposition to Disease/*genetics Genetics, Medical/*methods Genome, Human/*genetics Genotype HumansFeb Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pi(samples)) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) >or= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) >or= 0.01).fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16415888 F1061-4036 (Print) Journal Article Research Support, N.I.H., Extramural16415888Department of Biostatistics and Center for Statistical Genetics, University of Michigan, 1420 Washington Heights, Ann Arbor, Michigan 48109-2029, USA.6~?l4Skol, A. D. Scott, L. J. Abecasis, G. R. Boehnke, M.2006{Corrigendum: Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies390 Nat Genet38Alleles Case-Control Studies DNA Replication/*genetics Gene Frequency/genetics Genetic Heterogeneity Genetic Markers/genetics Genetic Predisposition to Disease/*genetics Genetics, Medical/*methods Genome, Human/*genetics Genotype HumansFeb Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pi(samples)) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) >or= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) >or= 0.01).fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16415888 F1061-4036 (Print) Journal Article Research Support, N.I.H., Extramural16415888Department of Biostatistics and Center for Statistical Genetics, University of Michigan, 1420 Washington Heights, Ann Arbor, Michigan 48109-2029, USA.~?mSladek, R. Rocheleau, G. Rung, J. Dina, C. Shen, L. Serre, D. Boutin, P. Vincent, D. Belisle, A. Hadjadj, S. Balkau, B. Heude, B. Charpentier, G. Hudson, T. J. Montpetit, A. Pshezhetsky, A. V. Prentki, M. Posner, B. I. Balding, D. J. Meyre, D. Polychronakos, C. Froguel, P.2007NA genome-wide association study identifies novel risk loci for type 2 diabetes881-5Nature4457130Case-Control Studies Cation Transport Proteins/genetics Chromosomes, Human, Pair 10/genetics Chromosomes, Human, Pair 8/genetics Diabetes Mellitus, Type 2/*genetics France Genetic Predisposition to Disease/*genetics *Genome, Human Humans Linkage DisequilibriumFeb 22Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case-control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing beta-cells, and two linkage disequilibrium blocks that contain genes potentially involved in beta-cell development or function (IDE-KIF11-HHEX and EXT2-ALX4). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17293876 G1476-4687 (Electronic) Journal Article Research Support, Non-U.S. Gov't17293876nDepartment of Human Genetics, McGill University and Genome Quebec Innovation Centre, Montreal H3A 1A4, Canada.u~?n Smith, C. A.1963LTesting for Heterogeneity of Recombination Fraction Values in Human Genetics175-82 Ann Hum Genet27F*Erythrocytes *Genetics, Medical *Hematology *Rh-Hr Blood-Group SystemNovfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14081488 !0003-4800 (Print) Journal Article14081488~?o%Smith, F. M. Garfield, A. S. Ward, A.20066Regulation of growth and metabolism by imprinted genes279-91Cytogenet Genome Res1131-4Animals Embryonic Development/*genetics *Gene Expression Regulation Gene Expression Regulation, Developmental *Genomic Imprinting Growth Disorders/*genetics Humans Metabolism/*genetics Mice Mice, Knockout SyndromeA small sub-set of mammalian genes are subject to regulation by genomic imprinting such that only one parental allele is active in at least some sites of expression. Imprinted genes have diverse functions, notably including the regulation of growth. Much attention has been devoted to the insulin-like growth factor signalling pathway that has a major influence on fetal size and contains two components encoded by the oppositely imprinted genes, Igf2 (a growth promoting factor expressed from the paternal allele) and Igf2r (a growth inhibitory factor expressed from the maternal allele). These genes fit the parent-offspring conflict hypothesis for the evolution of genomic imprinting. Accumulated evidence indicates that at least one other fetal growth pathway exists that has also fallen under the influence of imprinting. It is clear that not all components of growth regulatory pathways are encoded by imprinted genes and instead it may be that within a pathway the influence of a single gene by each of the parental genomes may be sufficient for parent-offspring conflict to be enacted. A number of imprinted genes have been found to influence energy homeostasis and some, including Igf2 and Grb10, may coordinate growth with glucose-regulated metabolism. Since perturbation of fetal growth can be correlated with metabolic disorders in adulthood these imprinted genes are considered as candidates for involvement in this phenomenon of fetal programming.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16575191 N1424-859X (Electronic) Journal Article Research Support, Non-U.S. Gov't Review16575191Centre for Regenerative Medicine and Developmental Biology Programme, Department of Biology and Biochemistry, University of Bath, Bath, UK.~?p-Snieder, H. van Doornen, L. J. Boomsma, D. I.1997ZThe age dependency of gene expression for plasma lipids, lipoproteins, and apolipoproteins638-50Am J Hum Genet603Adolescent Aged Aging/blood/*genetics Apolipoproteins/blood/*genetics Female Humans Lipids/blood/*genetics Lipoproteins/blood/*genetics Male Middle Aged Models, GeneticMardThe aim of this study was to investigate and disentangle the genetic and nongenetic causes of stability and change in lipids and (apo)lipoproteins that occur during the lifespan. Total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and lipoprotein(a) (Lp[a]) were measured in a group of 160 middle-aged parents and their twin offspring (first project) and in a group of 203 middle-aged twin pairs (second project). Combining the data of both projects enabled the estimation of the extent to which measured lipid parameters are influenced by different genes in adolescence and adulthood. To that end, an extended quantitative genetic model was specified, which allowed the estimation of heritabilities for each sex and generation separately. Heritabilities were similar for both sexes and both generations. Larger variances in the parental generation could be ascribed to proportional increases in both unique environmental and additive genetic variance from childhood to adulthood, which led to similar heritability estimates in adolescent and middle-aged twins. Although the magnitudes of heritabilities were similar across generations, results showed that, for total cholesterol, triglycerides, HDL, and LDL, partly different genes are expressed in adolescence compared to adulthood. For triglycerides, only 46% of the genetic variance was common to both age groups; for total cholesterol this was 80%. Intermediate values were found for HDL (66%) and LDL (76%). For ApoA1, ApoB, and Lp(a), the same genes seem to act in both generations.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9042925 M0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Twin Study9042925SDepartment of Psychophysiology, Vrije Universiteit, Amsterdam. h.snieder@umds.ac.uk(?q-Snieder, H. van Doornen, L.J.P. Boomsma, D.I1995^Developmental genetic trends in blood pressure levels and blood pressure reactivity to stress.105-1302Behavior Genetic Approaches in Behavioral Medicine#J.R. Turner L.R. Cardon J.K. Hewitt New York, NY Plenum Press~?rSobel, E. Lange, K.1996pDescent graphs in pedigree analysis: applications to haplotyping, location scores, and marker-sharing statistics1323-37Am J Hum Genet586Alleles *Chromosome Mapping Chromosomes, Human, Pair 12 Female *Genetic Markers *Haplotypes Humans Male Markov Chains *Models, Genetic *Models, Statistical *Pedigree Phenotype Probability Recombination, Genetic SoftwareJunThe introduction of stochastic methods in pedigree analysis has enabled geneticists to tackle computations intractable by standard deterministic methods. Until now these stochastic techniques have worked by running a Markov chain on the set of genetic descent states of a pedigree. Each descent state specifies the paths of gene flow in the pedigree and the founder alleles dropped down each path. The current paper follows up on a suggestion by Elizabeth Thompson that genetic descent graphs offer a more appropriate space for executing a Markov chain. A descent graph specifies the paths of gene flow but not the particular founder alleles traveling down the paths. This paper explores algorithms for implementing Thompson's suggestion for codominant markers in the context of automatic haplotyping, estimating location scores, and computing gene-clustering statistics for robust linkage analysis. Realistic numerical examples demonstrate the feasibility of the algorithms.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8651310 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.86513101Department of Genetics, Stanford University, USA.~?sSobel, E. Papp, J. C. Lange, K.2002FDetection and integration of genotyping errors in statistical genetics496-508Am J Hum Genet702Algorithms Chromosome Mapping/*methods/*statistics & numerical data Female Founder Effect Genotype Humans Male Markov Chains Models, Genetic Monte Carlo Method Paternity Pedigree *Research Design Software Stochastic Processes Twins/geneticsFebDetection of genotyping errors and integration of such errors in statistical analysis are relatively neglected topics, given their importance in gene mapping. A few inopportunely placed errors, if ignored, can tremendously affect evidence for linkage. The present study takes a fresh look at the calculation of pedigree likelihoods in the presence of genotyping error. To accommodate genotyping error, we present extensions to the Lander-Green-Kruglyak deterministic algorithm for small pedigrees and to the Markov-chain Monte Carlo stochastic algorithm for large pedigrees. These extensions can accommodate a variety of error models and refrain from simplifying assumptions, such as allowing, at most, one error per pedigree. In principle, almost any statistical genetic analysis can be performed taking errors into account, without actually correcting or deleting suspect genotypes. Three examples illustrate the possibilities. These examples make use of the full pedigree data, multiple linked markers, and a prior error model. The first example is the estimation of genotyping error rates from pedigree data. The second-and currently most useful-example is the computation of posterior mistyping probabilities. These probabilities cover both Mendelian-consistent and Mendelian-inconsistent errors. The third example is the selection of the true pedigree structure connecting a group of people from among several competing pedigree structures. Paternity testing and twin zygosity testing are typical applications.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11791215 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11791215`Department of 1Human Genetics, University of California, Los Angeles 90095, USA. esobel@ucla.edu~?t!Sobel, E. Sengul, H. Weeks, D. E.2001xMultipoint estimation of identity-by-descent probabilities at arbitrary positions among marker loci on general pedigrees121-31 Hum Hered523f*Algorithms Female Genotype Humans Male Meiosis *Models, Genetic Monte Carlo Method Pedigree *SoftwareuOBJECTIVES: To describe, implement, and test an efficient algorithm to obtain multipoint identity-by-descent (IBD) probabilities at arbitrary positions among marker loci for general pedigrees. Unlike existing programs, our algorithm can analyze data sets with large numbers of people and markers. The algorithm has been implemented in the SimWalk2 computer package. METHODS: Using a rigorous testing regimen containing five pedigrees of various sizes with realistic marker data, we compared several widely used IBD computation programs: Allegro, Aspex, GeneHunter, MapMaker/Sibs, Mendel, Sage, SimWalk2, and Solar. RESULTS: The testing revealed a few discrepancies, particularly on consanguineous pedigrees, but overall excellent results in the deterministic multipoint packages. SimWalk2 was also found to be in good agreement with the deterministic multipoint programs, usually matching to two decimal places the kinship coefficient that ranges from 0 to 1. However, the packages based on single-point IBD estimation, while consistent with each other, often showed poor results, disagreeing with the multipoint kinship results by as much as 0.5. CONCLUSIONS: Our testing has clearly shown that multipoint IBD estimation is much better than single-point estimation. In addition, our testing has validated our algorithm for estimating IBD probabilities at arbitrary positions on general pedigrees.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11588394 y0001-5652 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11588394MDepartment of Human Genetics, University of California, Los Angeles, CA, USA. ~?u)Sobell, J. L. Heston, L. L. Sommer, S. S.1993Novel association approach for determining the genetic predisposition to schizophrenia: case-control resource and testing of a candidate gene28-35Am J Med Genet481}Adult Age Factors Alleles Base Sequence Case-Control Studies Chi-Square Distribution DNA/analysis/chemistry Female Heterozygote Humans Linkage (Genetics) Male Middle Aged Molecular Sequence Data Mutation Oligodeoxyribonucleotides/chemistry Phenylalanine Hydroxylase/*genetics Polymerase Chain Reaction Risk Factors Schizophrenia/ethnology/*genetics Sex Factors Variation (Genetics)May 1We have developed a two-tiered approach to elucidating the genetic predisposition to schizophrenia. The approach first involves the examination of candidate genes in a subset of schizophrenic individuals to identify DNA sequence variations of likely functional significance, i.e., that produce either structural alterations in the protein or affect the level of gene expression. Once identified, the prevalence of the aberrant allele is examined in a large group of unrelated schizophrenic cases and controls to assess whether a true disease association exists. Herein, we describe the establishment of a DNA bank on nearly 200 unrelated schizophrenic cases defined by DSM-III-R criteria and on over 300 unrelated, ethnically similar controls. Characteristics of the study sample are described. The study approach then is illustrated by testing known mutations in the phenylalanine hydroxylase gene, responsible for the autosomal recessive disease of phenylketonuria, in the case-control sample to determine if carriership of a mutant allele is associated with an increased risk of schizophrenia. Using PCR amplification of specific alleles (PASA), we screened 190 schizophrenic cases and 336 controls for two common point mutations in the phenylalanine hydroxylase gene. Two carriers were found among the controls, while none of the cases was shown to carry a mutant allele. Thus, carriership of either of two common mutations in the phenylalanine hydroxylase gene does not appear to be associated with an increased risk of schizophrenia. As additional candidate genes are tested in this case-control resource, adjustment for multiple comparisons will become crucial in order to reduce the chance of false positive findings.(ABSTRACT TRUNCATED AT 250 WORDS)ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8357034 e0148-7299 (Print) Case Reports Journal Article Multicenter Study Research Support, U.S. Gov't, P.H.S.8357034TDepartment of Health Sciences Research, Mayo Clinic/Foundation, Rochester, MN 55905.?vSorensen, D. Gianola, D. 2002@Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics. New York, NYSpringer~?wSpielman, R. S. Ewens, W. J.1998cA sibship test for linkage in the presence of association: the sib transmission/disequilibrium test450-8Am J Hum Genet622Disease Susceptibility Female Genetic Diseases, Inborn/*genetics Genomic Imprinting Genotype Heterozygote Detection Humans *Linkage (Genetics) *Linkage Disequilibrium Male *Models, Genetic Models, Statistical *Nuclear FamilyFebLinkage analysis with genetic markers has been successful in the localization of genes for many monogenic human diseases. In studies of complex diseases, however, tests that rely on linkage disequilibrium (the simultaneous presence of linkage and association) are often more powerful than those that rely on linkage alone. This advantage is illustrated by the transmission/disequilibrium test (TDT). The TDT requires data (marker genotypes) for affected individuals and their parents; for some diseases, however, data from parents may be difficult or impossible to obtain. In this article, we describe a method, called the "sib TDT" (or "S-TDT"), that overcomes this problem by use of marker data from unaffected sibs instead of from parents, thus allowing application of the principle of the TDT to sibships without parental data. In a single collection of families, there might be some that can be analyzed only by the TDT and others that are suitable for analysis by the S-TDT. We show how all the data may be used jointly in one overall TDT-type procedure that tests for linkage in the presence of association. These extensions of the TDT will be valuable for the study of diseases of late onset, such as non-insulin-dependent diabetes, cardiovascular diseases, and other diseases associated with aging.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9463321 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9463321hDepartment of Genetics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104-6018, USA. &~?x,Spielman, R. S. McGinnis, R. E. Ewens, W. J.1993tTransmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM)506-16Am J Hum Genet523Adult Alleles Analysis of Variance Child Diabetes Mellitus, Type 1/*genetics Family Female Genetic Markers Heterozygote Detection Humans Insulin/*genetics *Linkage Disequilibrium Male Mathematics Repetitive Sequences, Nucleic AcidMar<A population association has consistently been observed between insulin-dependent diabetes mellitus (IDDM) and the "class 1" alleles of the region of tandem-repeat DNA (5' flanking polymorphism [5'FP]) adjacent to the insulin gene on chromosome 11p. This finding suggests that the insulin gene region contains a gene or genes contributing to IDDM susceptibility. However, several studies that have sought to show linkage with IDDM by testing for cosegregation in affected sib pairs have failed to find evidence for linkage. As means for identifying genes for complex diseases, both the association and the affected-sib-pairs approaches have limitations. It is well known that population association between a disease and a genetic marker can arise as an artifact of population structure, even in the absence of linkage. On the other hand, linkage studies with modest numbers of affected sib pairs may fail to detect linkage, especially if there is linkage heterogeneity. We consider an alternative method to test for linkage with a genetic marker when population association has been found. Using data from families with at least one affected child, we evaluate the transmission of the associated marker allele from a heterozygous parent to an affected offspring. This approach has been used by several investigators, but the statistical properties of the method as a test for linkage have not been investigated. In the present paper we describe the statistical basis for this "transmission test for linkage disequilibrium" (transmission/disequilibrium test [TDT]). We then show the relationship of this test to tests of cosegregation that are based on the proportion of haplotypes or genes identical by descent in affected sibs. The TDT provides strong evidence for linkage between the 5'FP and susceptibility to IDDM. The conclusions from this analysis apply in general to the study of disease associations, where genetic markers are usually closely linked to candidate genes. When a disease is found to be associated with such a marker, the TDT may detect linkage even when haplotype-sharing tests do not.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8447318 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8447318_Department of Genetics, University of Pennsylvania School of Medicine, Philadelphia 19104-6145.}~?yStefansson, H. Helgason, A. Thorleifsson, G. Steinthorsdottir, V. Masson, G. Barnard, J. Baker, A. Jonasdottir, A. Ingason, A. Gudnadottir, V. G. Desnica, N. Hicks, A. Gylfason, A. Gudbjartsson, D. F. Jonsdottir, G. M. Sainz, J. Agnarsson, K. Birgisdottir, B. Ghosh, S. Olafsdottir, A. Cazier, J. B. Kristjansson, K. Frigge, M. L. Thorgeirsson, T. E. Gulcher, J. R. Kong, A. Stefansson, K.2005/A common inversion under selection in Europeans129-37 Nat Genet372*Chromosomes, Human, Pair 17 European Continental Ancestry Group/*genetics Female Gene Frequency Haplotypes Humans Iceland *Inversion, Chromosome Molecular Sequence Data Phylogeny Physical Chromosome Mapping Polymorphism, Genetic Recombination, Genetic *Selection (Genetics)FebA refined physical map of chromosome 17q21.31 uncovered a 900-kb inversion polymorphism. Chromosomes with the inverted segment in different orientations represent two distinct lineages, H1 and H2, that have diverged for as much as 3 million years and show no evidence of having recombined. The H2 lineage is rare in Africans, almost absent in East Asians but found at a frequency of 20% in Europeans, in whom the haplotype structure is indicative of a history of positive selection. Here we show that the H2 lineage is undergoing positive selection in the Icelandic population, such that carrier females have more children and have higher recombination rates than noncarriers.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15654335 !1061-4036 (Print) Journal Article156543356deCODE Genetics, Sturlugata 8, 101 Reykjavik, Iceland.?zStephens, M Donnelly, P.2000+ Inference in molecular population genetics605-655J. R. Stat. Soc. Ser. B62.~?{Stephens, M. Donnelly, P.2003[A comparison of bayesian methods for haplotype reconstruction from population genotype data1162-9Am J Hum Genet735Algorithms *Bayes Theorem Genetics, Population/*methods Haplotypes/*genetics Humans Internet Models, Genetic Research Design SoftwareNovIn this report, we compare and contrast three previously published Bayesian methods for inferring haplotypes from genotype data in a population sample. We review the methods, emphasizing the differences between them in terms of both the models ("priors") they use and the computational strategies they employ. We introduce a new algorithm that combines the modeling strategy of one method with the computational strategies of another. In comparisons using real and simulated data, this new algorithm outperforms all three existing methods. The new algorithm is included in the software package PHASE, version 2.0, available online (http://www.stat.washington.edu/stephens/software.html).fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14574645 X0002-9297 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.14574645mDepartment of Statistics, University of Washington, Seattle, WA 98195-4322, USA. stephens@stat.washington.edu~?|Stephens, M. Scheet, P.2005aAccounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation449-62Am J Hum Genet763*Algorithms Alleles Biometry Chromosomes, Human, X/genetics Data Interpretation, Statistical Genomics/*statistics & numerical data Genotype Haplotypes/*genetics Humans *Linkage Disequilibrium Male *Models, Genetic SoftwareMarLAlthough many algorithms exist for estimating haplotypes from genotype data, none of them take full account of both the decay of linkage disequilibrium (LD) with distance and the order and spacing of genotyped markers. Here, we describe an algorithm that does take these factors into account, using a flexible model for the decay of LD with distance that can handle both "blocklike" and "nonblocklike" patterns of LD. We compare the accuracy of this approach with a range of other available algorithms in three ways: for reconstruction of randomly paired, molecularly determined male X chromosome haplotypes; for reconstruction of haplotypes obtained from trios in an autosomal region; and for estimation of missing genotypes in 50 autosomal genes that have been completely resequenced in 24 African Americans and 23 individuals of European descent. For the autosomal data sets, our new approach clearly outperforms the best available methods, whereas its accuracy in inferring the X chromosome haplotypes is only slightly superior. For estimation of missing genotypes, our method performed slightly better when the two subsamples were combined than when they were analyzed separately, which illustrates its robustness to population stratification. Our method is implemented in the software package PHASE (v2.1.1), available from the Stephens Lab Web site.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15700229 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.15700229mDepartment of Statistics, University of Washington, Seattle, WA 98195-4322, USA. stephens@stat.washington.edu~?}&Stephens, M. Smith, N. J. Donnelly, P.2001JA new statistical method for haplotype reconstruction from population data978-89Am J Hum Genet684*Algorithms Calibration Computer Simulation Gene Frequency/genetics Haplotypes/*genetics Humans Research Design Sensitivity and Specificity StatisticsApr/Current routine genotyping methods typically do not provide haplotype information, which is essential for many analyses of fine-scale molecular-genetics data. Haplotypes can be obtained, at considerable cost, experimentally or (partially) through genotyping of additional family members. Alternatively, a statistical method can be used to infer phase and to reconstruct haplotypes. We present a new statistical method, applicable to genotype data at linked loci from a population sample, that improves substantially on current algorithms; often, error rates are reduced by > 50%, relative to its nearest competitor. Furthermore, our algorithm performs well in absolute terms, suggesting that reconstructing haplotypes experimentally or by genotyping additional family members may be an inefficient use of resources.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11254454 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't11254454LDepartment of Statistics, University of Oxford. stephens@stat.washington.edu ~?~Stine, O. C. Xu, J. Koskela, R. McMahon, F. J. Gschwend, M. Friddle, C. Clark, C. D. McInnis, M. G. Simpson, S. G. Breschel, T. S. Vishio, E. Riskin, K. Feilotter, H. Chen, E. Shen, S. Folstein, S. Meyers, D. A. Botstein, D. Marr, T. G. DePaulo, J. R.1995XEvidence for linkage of bipolar disorder to chromosome 18 with a parent-of-origin effect1384-94Am J Hum Genet576Adolescent Adult Bipolar Disorder/*genetics Chromosome Mapping *Chromosomes, Human, Pair 18 Female Genotype Humans *Linkage (Genetics) Lod Score MaleDecA susceptibility gene on chromosome 18 and a parent-of-origin effect have been suggested for bipolar affective disorder (BPAD). We have studied 28 nuclear families selected for apparent unilineal transmission of the BPAD phenotype, by using 31 polymorphic markers spanning chromosome 18. Evidence for linkage was tested with affected-sib-pair and LOD score methods under two definitions of the affected phenotype. The affected-sibpair analyses indicated excess allele sharing for markers on 18p within the region reported previously. The greatest sharing was at D18S37: 64% in bipolar and recurrent unipolar (RUP) sib pairs (P = .0006). In addition, excess sharing of the paternally, but not maternally, transmitted alleles was observed at three markers on 18q: at D18S41, 51 bipolar and RUP sib pairs were concordant for paternally transmitted alleles, and 21 pairs were discordant (P = 0004). The evidence for linkage to loci on both 18p and 18q was strongest in the 11 paternal pedigrees, i.e., those in which the father or one of the father's sibs is affected. In these pedigrees, the greatest allele sharing (81%; P = .00002) and the highest LOD score (3.51; phi = 0.0) were observed at D18S41. Our results provide further support for linkage of BPAD to chromosome 18 and the first molecular evidence for a parent-of-origin effect operating in this disorder. The number of loci involved, and their precise location, require further study..ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8533768 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.8533768ZDepartment of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA.~?Storey, J. D. Tibshirani, R.2003/Statistical significance for genomewide studies9440-5Proc Natl Acad Sci U S A10016Algorithms Alternative Splicing Animals Binding Sites Exons Gene Expression Regulation *Genetic Techniques *Genome Humans Linkage (Genetics) Oligonucleotide Array Sequence Analysis/methods Statistics Transcription, GeneticAug 5With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12883005 !0027-8424 (Print) Journal Article12883005gDepartment of Biostatistics, University of Washington, Seattle, WA 98195, USA. jstorey@u.washington.edu7~?fStram, D. O. Haiman, C. A. Hirschhorn, J. N. Altshuler, D. Kolonel, L. N. Henderson, B. E. Pike, M. C.2003Choosing haplotype-tagging SNPS based on unphased genotype data using a preliminary sample of unrelated subjects with an example from the Multiethnic Cohort Study27-36 Hum Hered551Aromatase/*genetics Breast Neoplasms/*ethnology/genetics Case-Control Studies Cohort Studies Computer Simulation Female Genetic Predisposition to Disease Genotype Haplotypes/*genetics Humans Male Polymorphism, Single Nucleotide/*genetics Prostatic Neoplasms/*ethnology/geneticsWe describe an approach for picking haplotype-tagging single nucleotide polymorphisms (htSNPs) that is presently being taken in two large nested case-control studies within a multiethnic cohort (MEC), which are engaged in a search for associations between risk of prostate and breast cancer and common genetic variations in candidate genes. Based on a preliminary sample of 70 control subjects chosen at random from each of the 5 ethnic groups in the MEC we estimate haplotype frequencies using a variant of the Excoffier-Slatkin E-M algorithm after genotyping a high density of SNPs selected every 3-5 kb in and surrounding a candidate gene. In order to evaluate the performance of a candidate set of htSNPS (which will be genotyped in the much larger case-control sample) we treat the haplotype frequencies estimate above as known, and carry out a formal calculation of the uncertainty of the number of copies of common haplotypes carried by an individual, summarizing this calculation as a coefficient of determination, R2h. A candidate set of htSNPS of a given size is chosen so as to maximize the minimum value of R2h over the common haplotypes, h.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12890923 X0001-5652 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.12890923Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, Calif. 90033, USA. stram@usc.edux~?Stram, D. O. Lee, J. W.1994CVariance components testing in the longitudinal mixed effects model1171-7 Biometrics504Age Factors Biometry/methods Child Female Growth Humans *Longitudinal Studies Male *Models, Statistical Multivariate Analysis Pituitary Gland/anatomy & histology Probability Sex Characteristics Sex Factors Skull/anatomy & histologyDecThis article discusses the asymptotic behavior of likelihood ratio tests for nonzero variance components in the longitudinal mixed effects linear model described by Laird and Ware (1982, Biometrics 38, 963-974). Our discussion of the large-sample behavior of likelihood ratio tests for nonzero variance components is based on the results for nonstandard testing situations by Self and Liang (1987, Journal of the American Statistical Association 82, 605-610).ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7786999 X0006-341X (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.7786999YDepartment of Preventive Medicine, University of Southern California, Arcadia 91066-6012. ~?Stram, D. O. Leigh Pearce, C. Bretsky, P. Freedman, M. Hirschhorn, J. N. Altshuler, D. Kolonel, L. N. Henderson, B. E. Thomas, D. C.2003Modeling and E-M estimation of haplotype-specific relative risks from genotype data for a case-control study of unrelated individuals179-90 Hum Hered554DAlgorithms Breast Neoplasms/*ethnology/*genetics Case-Control Studies Cohort Studies Computer Simulation Female Genetic Predisposition to Disease Genotype Haplotypes/*genetics Humans Incidence Likelihood Functions *Models, Genetic Polymorphism, Single Nucleotide/*genetics Risk Factors Steroid 17-alpha-Hydroxylase/*geneticsThe US National Cancer Institute has recently sponsored the formation of a Cohort Consortium (http://2002.cancer.gov/scpgenes.htm) to facilitate the pooling of data on very large numbers of people, concerning the effects of genes and environment on cancer incidence. One likely goal of these efforts will be generate a large population-based case-control series for which a number of candidate genes will be investigated using SNP haplotype as well as genotype analysis. The goal of this paper is to outline the issues involved in choosing a method of estimating haplotype-specific risk estimates for such data that is technically appropriate and yet attractive to epidemiologists who are already comfortable with odds ratios and logistic regression. Our interest is to develop and evaluate extensions of methods, based on haplotype imputation, that have been recently described (Schaid et al., Am J Hum Genet, 2002, and Zaykin et al., Hum Hered, 2002) as providing score tests of the null hypothesis of no effect of SNP haplotypes upon risk, which may be used for more complex tasks, such as providing confidence intervals, and tests of equivalence of haplotype-specific risks in two or more separate populations. In order to do so we (1) develop a cohort approach towards odds ratio analysis by expanding the E-M algorithm to provide maximum likelihood estimates of haplotype-specific odds ratios as well as genotype frequencies; (2) show how to correct the cohort approach, to give essentially unbiased estimates for population-based or nested case-control studies by incorporating the probability of selection as a case or control into the likelihood, based on a simplified model of case and control selection, and (3) finally, in an example data set (CYP17 and breast cancer, from the Multiethnic Cohort Study) we compare likelihood-based confidence interval estimates from the two methods with each other, and with the use of the single-imputation approach of Zaykin et al. applied under both null and alternative hypotheses. We conclude that so long as haplotypes are well predicted by SNP genotypes (we use the Rh2 criteria of Stram et al. [1]) the differences between the three methods are very small and in particular that the single imputation method may be expected to work extremely well.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14566096 X0001-5652 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.14566096Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA. stram@usc.edu ~?Stranger, B. E. Forrest, M. S. Clark, A. G. Minichiello, M. J. Deutsch, S. Lyle, R. Hunt, S. Kahl, B. Antonarakis, S. E. Tavare, S. Deloukas, P. Dermitzakis, E. T.2005?Genome-wide associations of gene expression variation in humanse78 PLoS Genet16Chromosome Mapping/methods *Gene Expression Regulation Genetic Techniques *Genome, Human Humans Linkage (Genetics) Linkage Disequilibrium Models, Genetic Phenotype Polymorphism, Genetic Polymorphism, Single Nucleotide *Variation (Genetics)DecThe exploration of quantitative variation in human populations has become one of the major priorities for medical genetics. The successful identification of variants that contribute to complex traits is highly dependent on reliable assays and genetic maps. We have performed a genome-wide quantitative trait analysis of 630 genes in 60 unrelated Utah residents with ancestry from Northern and Western Europe using the publicly available phase I data of the International HapMap project. The genes are located in regions of the human genome with elevated functional annotation and disease interest including the ENCODE regions spanning 1% of the genome, Chromosome 21 and Chromosome 20q12-13.2. We apply three different methods of multiple test correction, including Bonferroni, false discovery rate, and permutations. For the 374 expressed genes, we find many regions with statistically significant association of single nucleotide polymorphisms (SNPs) with expression variation in lymphoblastoid cell lines after correcting for multiple tests. Based on our analyses, the signal proximal (cis-) to the genes of interest is more abundant and more stable than distal and trans across statistical methodologies. Our results suggest that regulatory polymorphism is widespread in the human genome and show that the 5-kb (phase I) HapMap has sufficient density to enable linkage disequilibrium mapping in humans. Such studies will significantly enhance our ability to annotate the non-coding part of the genome and interpret functional variation. In addition, we demonstrate that the HapMap cell lines themselves may serve as a useful resource for quantitative measurements at the cellular level.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16362079 l1553-7404 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16362079WWellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom. ~?Strauch, K. Baur, M. P.2005Parent-of-origin, imprinting, mitochondrial, and X-linked effects in traits related to alcohol dependence: presentation Group 18 of Genetic Analysis Workshop 14S125-32Genet Epidemiol 29 Suppl 1Alcoholism/*genetics Alleles Chromosome Mapping/methods *Chromosomes, Human, X Cytogenetic Analysis/*methods Data Interpretation, Statistical Genetic Predisposition to Disease/*genetics *Genomic Imprinting *Genotype Humans Mitochondria/*geneticsThe participants of Presentation Group 18 of Genetic Analysis Workshop 14 analyzed the Collaborative Study on the Genetics of Alcoholism data set to investigate sex-specific effects for phenotypes related to alcohol dependence. In particular, the participants looked at imprinting (which is also known as parent-of-origin effect), differences between recombination fractions for the two sexes, and mitochondrial and X-chromosomal effects. Five of the seven groups employed newly developed or existing methods that take imprinting into account when testing for linkage, or test for imprinting itself. Single-marker and multipoint analyses were performed for microsatellite as well as single-nucleotide polymorphism markers, and several groups used a sex-specific genetic map in addition to a sex-averaged map. Evidence for paternal imprinting (i.e., maternal expression) was consistently obtained by at least two groups at genetic regions on chromosomes 10, 12, and 21 that possibly harbor genes responsible for alcoholism. Evidence for maternal imprinting (which is equivalent to paternal expression) was consistently found at a locus on chromosome 11. Two groups applied extensions of variance components analysis that model a mitochondrial or X-chromosomal effect to latent class variables and electrophysiological traits employed in the diagnosis of alcoholism. The analysis, without using genetic markers, revealed mitochondrial or X-chromosomal effects for several of these traits. Accounting for sex-specific environmental variances appeared to be crucial for the identification of an X-chromosomal factor. In linkage analysis using marker data, modeling a mitochondrial variance component increased the linkage signals obtained for autosomal loci.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16342190 B0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't16342190|Institute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, Bonn, Germany. strauch@med.uni-marburg.de =~?LStrauch, K. Fimmers, R. Kurz, T. Deichmann, K. A. Wienker, T. F. Baur, M. P.2000Parametric and nonparametric multipoint linkage analysis with imprinting and two-locus-trait models: application to mite sensitization1945-57Am J Hum Genet666SAllergens/immunology Animals Chromosome Mapping/*methods/*statistics & numerical data Europe Female *Genetic Heterogeneity Genetic Markers/genetics Genomic Imprinting/*genetics Heterozygote Humans Hypersensitivity/*genetics/immunology Lod Score Male Mites/*immunology Models, Genetic Pedigree Penetrance Software *Statistics, NonparametricJunWe present two extensions to linkage analysis for genetically complex traits. The first extension allows investigators to perform parametric (LOD-score) analysis of traits caused by imprinted genes-that is, of traits showing a parent-of-origin effect. By specification of two heterozygote penetrance parameters, paternal and maternal origin of the mutation can be treated differently in terms of probability of expression of the trait. Therefore, a single-disease-locus-imprinting model includes four penetrances instead of only three. In the second extension, parametric and nonparametric linkage analysis with two trait loci is formulated for a multimarker setting, optionally taking imprinting into account. We have implemented both methods into the program GENEHUNTER. The new tools, GENEHUNTER-IMPRINTING and GENEHUNTER-TWOLOCUS, were applied to human family data for sensitization to mite allergens. The data set comprises pedigrees from England, Germany, Italy, and Portugal. With single-disease-locus-imprinting MOD-score analysis, we find several regions that show at least suggestive evidence for linkage. Most prominently, a maximum LOD score of 4.76 is obtained near D8S511, for the English population, when a model that implies complete maternal imprinting is used. Parametric two-trait-locus analysis yields a maximum LOD score of 6.09 for the German population, occurring exactly at D4S430 and D18S452. The heterogeneity model specified for analysis alludes to complete maternal imprinting at both disease loci. Altogether, our results suggest that the two novel formulations of linkage analysis provide valuable tools for genetic mapping of multifactorial traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10796874 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't10796874Institute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, 53105 Bonn, Germany. strauch@imsdd.meb.uni-bonn.de~?IStrauch, K. Fimmers, R. Windemuth, C. Hahn, A. Wienker, T. F. Baur, M. P.1999DLinkage analysis with adequate modeling of a parent-of-origin effectS331-6Genet Epidemiol 17 Suppl 1}Alcoholism/genetics Animals Genetic Screening *Genomic Imprinting Humans *Linkage (Genetics) Lod Score Mice Pedigree Software~We present an extension to parametric linkage analysis that allows modeling diseases with a parent-of-origin effect (i.e., imprinting). Different penetrances are assumed for individuals being heterozygous at the disease locus, depending on their having inherited the disease allele from the father or mother. Motivated by the finding of a maternally expressed locus influencing alcohol consumption in mice (Alcp2), the analysis method has been included into the program GENEHUNTER for application to Problem 1, Collaborative Study on the Genetics of Alcoholism of Genetic Analysis Workshop 11. By this extension, a powerful tool is provided for adequately modeling an inherited disease in linkage analysis that supposedly has imprinting effects. The program has been used to analyze the data set on alcohol dependence in humans and can be applied to other genetically determined traits as well.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10597458 B0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't10597458>Institute for Medical Statistics, University of Bonn, Germany.~?Stringham, H. M. Boehnke, M.1996?Identifying marker typing incompatibilities in linkage analysis946-50Am J Hum Genet594hAlleles Female *Genetic Markers *Genotype Humans *Linkage (Genetics) Male Pedigree Probability *SoftwareOctuA common problem encountered in linkage analyses is that execution of the computer program is halted because of genotypes in the data that are inconsistent with Mendelian inheritance. Such inconsistencies may arise because of pedigree errors or errors in typing. In some cases, the source of the inconsistencies is easily identified by examining the pedigree. In others, the error is not obvious, and substantial time and effort are required to identify the responsible genotypes. We have developed two methods for automatically identifying those individuals whose genotypes are most likely the cause of the inconsistencies. First, we calculate the posterior probability of genotyping error for each member of the pedigree, given the marker data on all pedigree members and allowing anyone in the pedigree to have an error. Second, we identify those individuals whose genotypes could be solely responsible for the inconsistency in the pedigree. We illustrate these methods with two examples: one a pedigree error, the second a genotyping error. These methods have been implemented as a module of the pedigree analysis program package MENDEL.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8808612 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.8808612hDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor 48109-2029, USA.~?Stringham, H. M. Boehnke, M.2001ELod scores for gene mapping in the presence of marker map uncertainty31-9Genet Epidemiol211Bias (Epidemiology) Chromosome Mapping/*methods/*standards Data Interpretation, Statistical Genetic Diseases, Inborn/epidemiology/genetics Genetic Markers/*genetics Humans Likelihood Functions *Lod Score *Models, Genetic PedigreeJulFMultipoint lod scores are typically calculated for a grid of locus positions, moving the putative disease locus across a fixed map of genetic markers. Changing the order of a set of markers and/or the distances between the markers can make a substantial difference in the resulting lod score curve and the location and height of its maximum. The typical approach of using the best maximum likelihood marker map is not easily justified if other marker orders are nearly as likely and give substantially different lod score curves. To deal with this problem, we propose three weighted multipoint lod score statistics that make use of information from all plausible marker orders. In each of these statistics, the information conditional on a particular marker order is included in a weighted sum, with weight equal to the posterior probability of that order. We evaluate the type 1 error rate and power of these three statistics on the basis of results from simulated data, and compare these results to those obtained using the best maximum likelihood map and the map with the true marker order. We find that the lod score based on a weighted sum of maximum likelihoods improves on using only the best maximum likelihood map, having a type 1 error rate and power closest to that of using the true marker order in the simulation scenarios we considered.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11443732 Y0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S. Validation Studies11443732DDepartment of Biostatistics, University of Michigan, Ann Arbor, USA.~?Stumpf, M. P. McVean, G. A.2003;Estimating recombination rates from population-genetic data959-68 Nat Rev Genet412T*Genetics, Population Humans Polymorphism, Single Nucleotide *Recombination, GeneticDecObtaining an accurate measure of how recombination rates vary across the genome has implications for understanding the molecular basis of recombination, its evolutionary significance and the distribution of linkage disequilibrium in natural populations. Although measuring the recombination rate is experimentally challenging, good estimates can be obtained by applying population-genetic methods to DNA sequences taken from natural populations. Statistical methods are now providing insights into the nature and scale of variation in the recombination rate, particularly in humans. Such knowledge will become increasingly important owing to the growing use of population-genetic methods in biomedical research.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14631356 I1471-0056 (Print) Journal Article Research Support, Non-U.S. Gov't Review14631356Department of Biological Sciences, Imperial College of Science, Technology and Medicine, London SW7 2AY, UK. m.stumpf@imperial.ac.uk~?Suarez, B. K. Hodge, S. E.1979bA simple method to detect linkage for rare recessive diseases: an application to juvenile diabetes126-36 Clin Genet152Diabetes Mellitus, Type 1/*genetics *Genes, Recessive Genetic Techniques HLA Antigens Humans *Linkage (Genetics) Recombination, GeneticFebA simple procedure designed specifically to detect linkage for rare recessive diseases is described. The method uses information on identity by descent scores for a pair of sibs at a marker locus conditioned on the number of affected sibs in the pair. A procedure for estimating the recombination fraction is described, and a table facilitating the likelihood ratio test of linkage is provided. The method, when applied to a collection of multiplex families segregating for juvenile diabetes mellitus, suggests the possibility that this disease is linked to the HLA complex. The method is found to compare favorably to the maximum likelihood approach, for which the computer program LIPED gives a maximum lod score of 2.48 at a male and female recombination fraction of theta = 0.20.dhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=367641 !0009-9163 (Print) Journal Article367641~? Suarez, B. K. Rice, J. Reich, T.1978NThe generalized sib pair IBD distribution: its use in the detection of linkage87-94 Ann Hum Genet421}Alleles Chromosome Mapping Gene Frequency *Genetic Diseases, Inborn Humans *Linkage (Genetics) *Models, Biological StatisticsJulGeneral expression for the distribution of identity by descent (IBD) scores at a marker locus have been derived given neither, one or both sibs affected with a disorder determined by a linked trait locus with arbitrary gene frequency and penetrance vector. It is shown that the distirbution of IBD scores depends only on the additive and dominance variances and the population prevalence of the disorder. A one-sided test is suggested as an appropriate means of statistically testing the hypothesis that the recombination fraction is significantly less than 1/2. This sib pair approach is designed primarily to detect the presence of a critical disease susceptibility locus but when the assumptions of the incompletely penetrant single locus model are correct the methodology proposed here results in consistent estimates of the recombination fraction. The affected sib pair methodology seems especially suited to traits determined by single loci with non-Mendelian transmission.dhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=686687 F0003-4800 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.686687~?8Sullivan, P. F. Eaves, L. J. Kendler, K. S. Neale, M. C.2001;Genetic case-control association studies in neuropsychiatry1015-24Arch Gen Psychiatry5811Brain/*physiopathology *Case-Control Studies Genetic Markers Humans Mental Disorders/*genetics/*physiopathology Molecular Biology/*methods Risk FactorsNovCase-control association studies use genetic markers as putative etiologic risk factors. The approach is controversial and has tended to produce associations in neuropsychiatry that do not stand the test of time. We studied the processes that can bias the outcomes away from a true representation of the relationship between a genetic marker and a neuropsychiatric disorder. If conducted with care and mindfulness of the potential pitfalls, case-control association studies can be an important tool for psychiatric genetic research.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11695947 M0003-990X (Print) Journal Article Research Support, U.S. Gov't, P.H.S. Review11695947Virginia Institute for Psychiatric and Behavioral Genetics, PO Box 980126, Richmond, VA 23298-0126. sullivan@psycho.psi.vcu.edu~?ISullivan, P. F. Neale, B. M. Neale, M. C. van den Oord, E. Kendler, K. S.2003OMultipoint and single point non-parametric linkage analysis with imperfect data89-94%Am J Med Genet B Neuropsychiatr Genet1211nComputer Simulation *Data Interpretation, Statistical *Linkage (Genetics) Lod Score *Statistics, NonparametricAug 15We used simulation to explore the impact of common data imperfections (i.e., missing parents, genotyping error, map error, and missing genotypes) upon the performance of multipoint and single point linkage analysis in the analyses of linkage data from pairs of siblings affected with an idealized complex trait. The performance of single point and multipoint linkage was similar under an unrealistic best case scenario; however, when four data imperfections were combined, the performance of single point linkage analysis appeared to be superior to multipoint. The absence of parental genotypes in the presence of 1% genotype error led to marked degradation of linkage signal, particularly for multipoint analyses.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12898581 X1552-4841 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.12898581Departments of Genetics and Psychiatry, University of North Carolina at Chapel Hill, North Carolina 27599-7264, USA. pfsulliv@med.unc.edu\~?)Surani, M. A. Barton, S. C. Norris, M. L.1984^Development of reconstituted mouse eggs suggests imprinting of the genome during gametogenesis548-50Nature3085959Animals Cell Nucleus/*physiology Embryo/*physiology Female *Fertilization *Genes Genotype Haploidy Male Mice Mice, Inbred Strains Ovum/*physiology ParthenogenesisApr 5-11UIt has been suggested that the failure of parthenogenetic mouse embryos to develop to term is primarily due to their aberrant cytoplasm and homozygosity leading to the expression of recessive lethal genes. The reported birth of homozygous gynogenetic (male pronucleus removed from egg after fertilization) mice and of animals following transplantation of nuclei from parthenogenetic embryos to enucleated fertilized eggs, is indicative of abnormal cytoplasm and not an abnormal genotype of the activated eggs. However, we and others have been unable to obtain such homozygous mice. We investigated this problem further by using reconstituted heterozygous eggs, with haploid parthenogenetic eggs as recipients for a male or female pronucleus. We report here that the eggs which receive a male pronucleus develop to term but those with two female pronuclei develop only poorly after implantation. Therefore, the cytoplasm of activated eggs is fully competent to support development to term but not if the genome is entirely of maternal origin. We propose that specific imprinting of the genome occurs during gametogenesis so that the presence of both a male and a female pronucleus is essential in an egg for full-term development. The paternal imprinting of the genome appears necessary for the normal development of the extraembryonic membranes and the trophoblast.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=6709062 !0028-0836 (Print) Journal Article6709062~? Sved, J. A.1971TLinkage disequilibrium and homozygosity of chromosome segments in finite populations125-41Theor Popul Biol22Alleles Chromosomes Female Gene Frequency Genes *Genetics, Population *Homozygote Humans *Linkage (Genetics) Male Ovum Polymorphism, Genetic Probability SpermatozoaJunehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=5170716 !0040-5809 (Print) Journal Article5170716 ~?Szatmari, P. Paterson, A. D. Zwaigenbaum, L. Roberts, W. Brian, J. Liu, X. Q. Vincent, J. B. Skaug, J. L. Thompson, A. P. Senman, L. Feuk, L. Qian, C. Bryson, S. E. Jones, M. B. Marshall, C. R. Scherer, S. W. Vieland, V. J. Bartlett, C. Mangin, L. V. Goedken, R. Segre, A. Pericak-Vance, M. A. Cuccaro, M. L. Gilbert, J. R. Wright, H. H. Abramson, R. K. Betancur, C. Bourgeron, T. Gillberg, C. Leboyer, M. Buxbaum, J. D. Davis, K. L. Hollander, E. Silverman, J. M. Hallmayer, J. Lotspeich, L. Sutcliffe, J. S. Haines, J. L. Folstein, S. E. Piven, J. Wassink, T. H. Sheffield, V. Geschwind, D. H. Bucan, M. Brown, W. T. Cantor, R. M. Constantino, J. N. Gilliam, T. C. Herbert, M. Lajonchere, C. Ledbetter, D. H. Lese-Martin, C. Miller, J. Nelson, S. Samango-Sprouse, C. A. Spence, S. State, M. Tanzi, R. E. Coon, H. Dawson, G. Devlin, B. Estes, A. Flodman, P. Klei, L. McMahon, W. M. Minshew, N. Munson, J. Korvatska, E. Rodier, P. M. Schellenberg, G. D. Smith, M. Spence, M. A. Stodgell, C. Tepper, P. G. Wijsman, E. M. Yu, C. E. Roge, B. Mantoulan, C. Wittemeyer, K. Poustka, A. Felder, B. Klauck, S. M. Schuster, C. Poustka, F. Bolte, S. Feineis-Matthews, S. Herbrecht, E. Schmotzer, G. Tsiantis, J. Papanikolaou, K. Maestrini, E. Bacchelli, E. Blasi, F. Carone, S. Toma, C. Van Engeland, H. de Jonge, M. Kemner, C. Koop, F. Langemeijer, M. Hijimans, C. Staal, W. G. Baird, G. Bolton, P. F. Rutter, M. L. Weisblatt, E. Green, J. Aldred, C. Wilkinson, J. A. Pickles, A. Le Couteur, A. Berney, T. McConachie, H. Bailey, A. J. Francis, K. Honeyman, G. Hutchinson, A. Parr, J. R. Wallace, S. Monaco, A. P. Barnby, G. Kobayashi, K. Lamb, J. A. Sousa, I. Sykes, N. Cook, E. H. Guter, S. J. Leventhal, B. L. Salt, J. Lord, C. Corsello, C. Hus, V. Weeks, D. E. Volkmar, F. Tauber, M. Fombonne, E. Shih, A.2007MMapping autism risk loci using genetic linkage and chromosomal rearrangements319-28 Nat Genet393Autistic Disorder/diagnosis/*genetics *Chromosome Aberrations *Chromosome Mapping Family Female *Genetic Predisposition to Disease Genetic Screening/*methods Humans *Linkage (Genetics) Lod Score Male Risk Factors Variation (Genetics)MarAutism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,168 families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17322880 y1061-4036 (Print) Comparative Study Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't17322880oDepartment of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario L8N 3Z5, Canada. ~?YTanck, M. W. Klerkx, A. H. Jukema, J. W. De Knijff, P. Kastelein, J. J. Zwinderman, A. H.2003Estimation of multilocus haplotype effects using weighted penalised log-likelihood: analysis of five sequence variations at the cholesteryl ester transfer protein gene locus175-84 Ann Hum Genet67Pt 2)Alleles Carrier Proteins/*genetics Cholesterol Ester Transfer Proteins DNA/metabolism Gene Frequency Genotype *Glycoproteins *Haplotypes Heterozygote Humans Linkage Disequilibrium Lipoproteins, HDL/genetics *Models, Genetic Models, Statistical Phenotype Polymorphism, Genetic *Variation (Genetics)MarDirect analyses of haplotype effects can be used to identify those specific combinations of alleles that are associated with a specific phenotype. We introduce a method for direct haplotype analysis that solves two problems that arise when haplotypes are analysed in populations of unrelated subjects. Instead of assigning a single, most likely, haplotype pair to multiple heterozygous subjects, all haplotype pairs compatible with their genotype were determined and the posterior probabilities of these pairs were calculated using Bayes' theorem and estimated haplotype frequencies. For the individual patients, all possible haplotype pairs were included in the statistical analysis using the posterior probabilities as weights, which were re-estimated in an iterative process together with the haplotype effects. The second problem of unstable haplotype effect estimates, due to the numerous haplotypes and the low frequency at which some occur, was solved by assuming that haplotypes sharing the same alleles show a similar effect and that the extent of this similarity relates to the number of alleles shared. These assumptions were incorporated in a weighted log-likelihood model by introducing a penalty, where differences in effects of similar haplotypes were penalised. Using CETP gene haplotypes, consisting of five closely linked polymorphisms, and baseline CETP and HDL-C concentrations from the REGRESS population, we demonstrated that the model resulted in more stable effects than estimates based on unambiguous patients only.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12675692 B0003-4800 (Print) Journal Article Research Support, Non-U.S. Gov't12675692pDepartment of Medical Statistics, Leiden University Medical Center (LUMC), The Netherlands. m.w.tanck@amc.uva.nl ~?&Tavtigian, S. V. Simard, J. Teng, D. H. Abtin, V. Baumgard, M. Beck, A. Camp, N. J. Carillo, A. R. Chen, Y. Dayananth, P. Desrochers, M. Dumont, M. Farnham, J. M. Frank, D. Frye, C. Ghaffari, S. Gupte, J. S. Hu, R. Iliev, D. Janecki, T. Kort, E. N. Laity, K. E. Leavitt, A. Leblanc, G. McArthur-Morrison, J. Pederson, A. Penn, B. Peterson, K. T. Reid, J. E. Richards, S. Schroeder, M. Smith, R. Snyder, S. C. Swedlund, B. Swensen, J. Thomas, A. Tranchant, M. Woodland, A. M. Labrie, F. Skolnick, M. H. Neuhausen, S. Rommens, J. Cannon-Albright, L. A.2001AA candidate prostate cancer susceptibility gene at chromosome 17p172-80 Nat Genet272Amino Acid Sequence Chromosomes, Human, Pair 17/*genetics Cloning, Molecular/methods DNA, Complementary/genetics Founder Effect Genetic Predisposition to Disease Genotype Humans Linkage (Genetics) Male Molecular Sequence Data Mutation, Missense Neoplasm Proteins/*genetics Pedigree Prostatic Neoplasms/*genetics RNA, Messenger/genetics Sequence Analysis, DNA Sequence Homology, Amino Acid UtahFeb>It is difficult to identify genes that predispose to prostate cancer due to late age at diagnosis, presence of phenocopies within high-risk pedigrees and genetic complexity. A genome-wide scan of large, high-risk pedigrees from Utah has provided evidence for linkage to a locus on chromosome 17p. We carried out positional cloning and mutation screening within the refined interval, identifying a gene, ELAC2, harboring mutations (including a frameshift and a nonconservative missense change) that segregate with prostate cancer in two pedigrees. In addition, two common missense variants in the gene are associated with the occurrence of prostate cancer. ELAC2 is a member of an uncharacterized gene family predicted to encode a metal-dependent hydrolase domain that is conserved among eukaryotes, archaebacteria and eubacteria. The gene product bears amino acid sequence similarity to two better understood protein families, namely the PSO2 (SNM1) DNA interstrand crosslink repair proteins and the 73-kD subunit of mRNA 3' end cleavage and polyadenylation specificity factor (CPSF73).fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11175785 y1061-4036 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.11175785BMyriad Genetics, Inc., Salt Lake City, Utah, USA. seant@myriad.com~? Templeton, A.2002Out of Africa again and again45-51Nature4166876Africa Americas Animals Asia DNA, Mitochondrial Eosinophil-Derived Neurotoxin Europe *Evolution *GTP-Binding Proteins Gene Frequency Genome, Human Haplotypes Hominidae Humans Models, Biological Models, Statistical Phylogeny Proteins/genetics Pyruvate Dehydrogenase (Lipoamide)/genetics Receptors, Corticotropin/genetics Receptors, Melanocortin Ribonucleases/genetics X Chromosome Y ChromosomeMar 7The publication of a haplotype tree of human mitochondrial DNA variation in 1987 provoked a controversy about the details of recent human evolution that continues to this day. Now many haplotype trees are available, and new analytical techniques exist for testing hypotheses about recent evolutionary history using haplotype trees. Here I present formal statistical analysis of human haplotype trees for mitochondrial DNA, Y-chromosomal DNA, two X-linked regions and six autosomal regions. A coherent picture of recent human evolution emerges with two major themes. First is the dominant role that Africa has played in shaping the modern human gene pool through at least two--not one--major expansions after the original range extension of Homo erectus out of Africa. Second is the ubiquity of genetic interchange between human populations, both in terms of recurrent gene flow constrained by geographical distance and of major population expansion events resulting in interbreeding, not replacement.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11882887 B0028-0836 (Print) Journal Article Research Support, Non-U.S. Gov't11882887nDepartment of Biology, Washington University, St Louis, Missouri 63130-4899, USA. temple_a@biology.wustl.edu). ~?+Templeton, A. R. Boerwinkle, E. Sing, C. F.1987A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping. I. Basic theory and an analysis of alcohol dehydrogenase activity in Drosophila343-51Genetics1172Alcohol Dehydrogenase/*genetics Animals DNA Restriction Enzymes Drosophila melanogaster/enzymology/*genetics *Genes *Haplotypes *Models, Genetic Nucleotide Mapping PhenotypeOctBecause some genes have been cloned that have a known biochemical or physiological function, genetic variation can be measured in a population at loci that may directly influence a phenotype of interest. With this measured genotype approach, specific alleles or haplotypes in the probed DNA region can be assigned phenotypic effects. In this paper we address several problems encountered in implementing the measured genotype approach with restriction site data. A number of analytical problems arise in part as a consequence of the linkage disequilibrium that is commonly encountered when dealing with small DNA regions: 1) different restriction site polymorphisms are not statistically independent, 2) the sites being measured are not likely to be the direct cause of the associated phenotypic effects, 3) haplotype classes may be phenotypically heterogeneous, and 4) the sites that are most strongly associated with phenotypic effects are not necessarily the most closely linked to the actual genetic cause of the effects. When recombination and gene conversion are rare, the primary cause of linkage disequilibrium is history (mutational origin, genetic drift, hitchhiking, etc.). We deal with historical association directly by producing a cladogram that partially reconstructs the evolutionary history of the present-day haplotype variability. The cladogram defines a nested analysis of variance that simultaneously detects phenotypic effects, localizes the effects within the cladogram, and identifies haplotypes that are potentially heterogeneous in their phenotypic associations. The power of this approach is illustrated by an analysis of the associations between alcohol dehydrogenase (ADH) activity and restriction site variability in a 13-kb fragment surrounding the ADH locus in Drosophila melanogaster.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2822535 F0016-6731 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.2822535KDepartment of Human Genetics, University of Michigan, Ann Arbor 48109-0618. /~?,Templeton, A. R. Crandall, K. A. Sing, C. F.1992A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. III. Cladogram estimation619-33Genetics13225Alcohol Dehydrogenase/genetics Algorithms Amylases/genetics Animals Carboxylesterase Carboxylic Ester Hydrolases/genetics Drosophila melanogaster/enzymology/genetics Genetic Techniques/statistics & numerical data *Haplotypes Mutation Phenotype Recombination, Genetic Restriction Mapping Sequence Analysis, DNAOctWe previously developed a cladistic approach to identify subsets of haplotypes defined by restriction endonuclease mapping or DNA sequencing that are associated with significant phenotypic deviations. Our approach was limited to segments of DNA in which little recombination occurs. In such cases, a cladogram can be constructed from the restriction site or sequence data that represents the evolutionary steps that interrelate the observed haplotypes. The cladogram is used to define a nested statistical design to identify mutational steps associated with significant phenotypic deviations. The central assumption behind this strategy is that any undetected mutation causing a phenotypic effect is embedded within the same evolutionary history that is represented by the cladogram. The power of this approach depends upon the confidence one has in the particular cladogram used to draw inferences. In this paper, we present a strategy for estimating the set of cladograms that are consistent with a particular sample of either restriction site or nucleotide sequence data and that includes the possibility of recombination. We first evaluate the limits of parsimony in constructing cladograms. Once these limits have been determined, we construct the set of parsimonious and nonparsimonious cladograms that is consistent with these limits. Our estimation procedure also identifies haplotypes that are candidates for being products of recombination. If recombination is extensive, our algorithm subdivides the DNA region into two or more subsections, each having little or no internal recombination. We apply this estimation procedure to three data sets to illustrate varying degrees of cladogram ambiguity and recombination.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1385266 o0016-6731 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.1385266HDepartment of Biology, Washington University, St. Louis, Missouri 63130. ~?Templeton, A. R. Sing, C. F.1993A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping. IV. Nested analyses with cladogram uncertainty and recombination659-69Genetics1342(*Algorithms Animals Carboxylesterase Carboxylic Ester Hydrolases/genetics Drosophila melanogaster/enzymology/genetics Haplotypes/*genetics Isoenzymes/genetics *Models, Genetic *Phenotype Polymorphism, Restriction Fragment Length *Recombination, Genetic *Restriction Mapping alpha-Amylase/geneticsJunWe previously developed an analytical strategy based on cladistic theory to identify subsets of haplotypes that are associated with significant phenotypic deviations. Our initial approach was limited to segments of DNA in which little recombination occurs. In such cases, a cladogram can be constructed from the restriction site data to estimate the evolutionary steps that interrelate the observed haplotypes to one another. The cladogram is then used to define a nested statistical design for identifying mutational steps associated with significant phenotypic deviations. The central assumption behind this strategy is that a mutation responsible for a particular phenotypic effect is embedded within the evolutionary history that is represented by the cladogram. The power of this approach depends on the accuracy of the cladogram in portraying the evolutionary history of the DNA region. This accuracy can be diminished both by recombination and by uncertainty in the estimated cladogram topology. In a previous paper, we presented an algorithm for estimating the set of likely claodgrams and recombination events. In this paper we present an algorithm for defining a nested statistical design under cladogram uncertainty and recombination. Given the nested design, phenotypic associations can be examined using either a nested analysis of variance (for haploids or homozygous strains) or permutation testing (for outcrossed, diploid gene regions). In this paper we also extend this analytical strategy to include categorical phenotypes in addition to quantitative phenotypes. Some worked examples are presented using Drosophila data sets. These examples illustrate that having some recombination may actually enhance the biological inferences that may derived from a cladistic analysis. In particular, recombination can be used to assign a physical localization to a given subregion for mutations responsible for significant phenotypic effects.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8100789 F0016-6731 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8100789HDepartment of Biology, Washington University, St. Louis, Missouri 63130. f~?7Templeton, A. R. Sing, C. F. Kessling, A. Humphries, S.1988A cladistic analysis of phenotype associations with haplotypes inferred from restriction endonuclease mapping. II. The analysis of natural populations1145-54Genetics1204>Alcohol Dehydrogenase/genetics Algorithms Animals Cholesterol/blood/genetics Data Interpretation, Statistical Drosophila melanogaster/genetics Female Genetic Techniques *Genetics, Population *Haplotypes Heterozygote Humans Male Phenotype Phylogeny Restriction Mapping Triglycerides/blood/genetics *Variation (Genetics)DecGenes that code for products involved in the physiology of a phenotype are logical candidates for explaining interindividual variation in that phenotype. We present a methodology for discovering associations between genetic variation at such candidate loci (assayed through restriction endonuclease mapping) with phenotypic variation at the population level. We confine our analyses to DNA regions in which recombination is very rare. In this case, the genetic variation at the candidate locus can be organized into a cladogram that represents the evolutionary relationships between the observed haplotypes. Any mutation causing a significant phenotypic effect should be imbedded within the same historical structure defined by the cladogram. We showed, in the first paper of this series, how to use the cladogram to define a nested analysis of variance (NANOVA) that was very efficient at detecting and localizing phenotypically important mutations. However, the NANOVA of haplotype effects could only be applied to populations of homozygous genotypes. In this paper, we apply the quantitative genetic concept of average excess to evaluate the phenotypic effect of a haplotype or group of haplotypes stratified and contrasted according to the nested design defined by the cladogram. We also show how a permutational procedure can be used to make statistical inferences about the nested average excess values in populations containing heterozygous as well as homozygous genotypes. We provide two worked examples that investigate associations between genetic variation at or near the Alcohol dehydrogenase (Adh) locus and Adh activity in Drosophila melanogaster, and associations between genetic variation at or near some apolipoprotein loci and various lipid phenotypes in a human population.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3147219 X0016-6731 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.3147219HDepartment of Biology, Washington University, St. Louis, Missouri 63130. ~?ITempleton, A. R. Weiss, K. M. Nickerson, D. A. Boerwinkle, E. Sing, C. F.2000tCladistic structure within the human Lipoprotein lipase gene and its implications for phenotypic association studies1259-75Genetics1563NAfrican Americans African Continental Ancestry Group/*genetics Base Sequence European Continental Ancestry Group/*genetics Finland Gene Conversion Genotype Haplotypes Humans Linkage Disequilibrium Lipoprotein Lipase/*genetics Minnesota Mississippi *Models, Genetic Phenotype Point Mutation Recombination, Genetic *Variation (Genetics)NovHaplotype variation in 9.7 kb of genomic DNA sequence from the human lipoprotein lipase (LPL) gene was scored in three populations: African-Americans from Jackson, Mississippi (24 individuals), Finns from North Karelia, Finland (24), and non-Hispanic whites from Rochester, Minnesota (23). Earlier analyses had indicated that recombination was common but concentrated into a hotspot and that recurrent mutations at multiple sites may have occurred. We show that much evolutionary structure exists in the haplotype variation on either side of the recombinational hotspot. By peeling off significant recombination events from a tree estimated under the null hypothesis of no recombination, we also reveal some cladistic structure not disrupted by recombination during the time to coalescence of this variation. Additional cladistic structure is estimated to have emerged after recombination. Many apparent multiple mutational events at sites still remain after removing the effects of the detected recombination/gene conversion events. These apparent multiple events are found primarily at sites identified as highly mutable by previous studies, strengthening the conclusion that they are true multiple events. This analysis portrays the complexity of the interplay among many recombinational and mutational events that would be needed to explain the patterns of haplotype diversity in this gene. The cladistic structure in this region is used to identify four to six single-nucleotide polymorphisms (SNPs) that would provide disequilibrium coverage over much of this region. These sites may be useful in identifying phenotypic associations with variable sites in this gene. Evolutionary considerations also imply that the SNPs in the 3' region should have general utility in most human populations, but the 5' SNPs may be more population specific. Choosing SNPs at random would generally not provide adequate disequilibrium coverage of the sequenced region.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11063700 F0016-6731 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11063700mDepartment of Biology, Washington University, St. Louis, Missouri 63130-4899, USA. temple_a@biology.wustl.edu?Terwilliger, J. D.1996aSIBPAIR: sibpair analysis on nuclear families. http://www.helsinki.fi/~tsjuntun/linkage/analyze/.R~?#Terwilliger, J. D. Ding, Y. Ott, J.1992\On the relative importance of marker heterozygosity and intermarker distance in gene mapping951-6Genomics134G*Genetic Markers *Heterozygote Lod Score Meiosis Recombination, GeneticAugMolecular biologists are often confronted with the problem of whether they should try to generate large numbers of very closely linked markers of low heterozygosity or smaller numbers of less closely linked markers of high heterozygosity. In other words, What is more important for gene mapping, high marker heterozygosity or dense marker spacing? We investigated that problem by analytically computing the expected lod score per meiosis in which the new locus is informative and phase known. We also looked at the length of the 1-unit-of-lod-score support interval for the expected lod score from 100 such meioses. We found that while both quantities have an influence on the number of meioses needed to find linkage, the length of the support interval is almost entirely dependent on the intermarker distance, for heterozygosities between 20 and 100%. However, the probability of any given meiosis being phase known and the ability to develop an accurate map of the markers are functions of marker heterozygosity, further complicating the issue.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1505986 g0888-7543 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.1505986VDepartment of Genetics and Development, Columbia University, New York, New York 10032.?Terwilliger, J.D. Ott, J.1994"Handbook of Human Genetic Linkage.237-238 Baltimore, MDJohns Hopkins University Press@~?%Thomas, D. C. Haile, R. W. Duggan, D.2005RRecent developments in genomewide association scans: a workshop summary and review337-45Am J Hum Genet773Data Interpretation, Statistical Genetic Diseases, Inborn/*genetics Genetic Markers/genetics Genetic Screening/*methods/*trends Genomics/*methods/*trends Genotype Research DesignSepMWith the imminent availability of ultra-high-volume genotyping platforms (on the order of 100,000-1,000,000 genotypes per sample) at a manageable cost, there is growing interest in the possibility of conducting genomewide association studies for a variety of diseases but, so far, little consensus on methods to design and analyze them. In April 2005, an international group of >100 investigators convened at the University of Southern California over the course of 2 days to compare notes on planned or ongoing studies and to debate alternative technologies, study designs, and statistical methods. This report summarizes these discussions in the context of the relevant literature. A broad consensus emerged that the time was now ripe for launching such studies, and several common themes were identified--most notably the considerable efficiency gains of multistage sampling design, specifically those made by testing only a portion of the subjects with a high-density genomewide technology, followed by testing additional subjects and/or additional SNPs at regions identified by this initial scan.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16080110 0002-9297 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Review16080110vDepartment of Preventive Medicine, University of Southern California, Los Angeles, CA 90089-9011, USA. dthomas@usc.edu? Thompson, W.R1966.Multivariate experiment in Behavior Genetics. 711-7310Handbook of Multivariate Experimental Psychology R.B. Cattell Chicago, IL Rand McNally ~?Tikhonoff, V. Kuznetsova, T. Stolarz, K. Bianchi, G. Casiglia, E. Kawecka-Jaszcz, K. Nikitin, Y. Tizzone, L. Wang, J. G. Staessen, J. A.2003^beta-Adducin polymorphisms, blood pressure, and sodium excretion in three European populations840-6Am J Hypertens1610#Adolescent Adult Blood Pressure/*genetics Calmodulin-Binding Proteins/*genetics Europe/epidemiology Family Health Female Gene Frequency Genotype Humans Hypertension/ethnology/*genetics/urine Male Middle Aged Phenotype *Polymorphism, Genetic Prevalence Sodium, Dietary/*pharmacokinetics/urineOctaThe associations of the beta-adducin C1797T polymorphism with blood pressure (BP) and various indexes of sodium homeostasis were investigated in 388 men and 456 women, aged 18 to 60 years, recruited from three European populations (Cracow, Poland, n = 300; Novosibirsk, Russian Federation, n = 274; Mirano, Italy; n = 270). Phenotypes included 24-h ambulatory BP and urinary excretion of electrolytes and aldosterone. Subjects were genotyped for the beta-adducin polymorphism. Both a population-based association study and a family-based analysis were performed. Urinary sodium excretion was higher in Cracow than in Mirano (241 v 185 mmol/24 h, P <.05) and intermediate in Novosibirsk (206 mmol/24 h). The beta-adducin T allele (15.2% v 9.1%, P <.0001) was more prevalent in Mirano than in the two Slavic centers. In both population-based and family-based association analyses, there was significant heterogeneity between Slavic and Italian subjects in the phenotype-genotype relationships with beta-adducin. In the Slavic centers, 24-h systolic BP was higher in T allele carriers than in CC homozygotes (122.3 v 119.7 mm Hg, P =.03), whereas this was not the case in Mirano (121.8 v 122.9 mm Hg, P =.42). In Slavic (212.6 v 233.1 mmol/24 h) as well as in Italian (166.1 v 191.5 mmol/24 h) participants, 24-h sodium excretion was lower (P =.01) in T allele carriers than in CC homozygotes. These results were confirmed in the family-based analysis of offspring using a quantitative transmission disequilibrium test. In conclusion, the frequency of the beta-adducin T allele and salt intake differ across European populations. Thus, both variation in genetic background and salt intake may explain the observed heterogeneity in the phenotype-genotype relationships. Genetic determinants of complex quantitative traits such as BP can only be investigated within their epidemiologic context.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14553963 !0895-7061 (Print) Journal Article14553963Hypertension and Cardiovascular Rehabilitation Unit, Department of Molecular and Cardiovascular Research, University of Leuven, Leuven, Belgium. valerie.tikhonoff@unipd.it~?Tishkoff, S. A. Reed, F. A. Ranciaro, A. Voight, B. F. Babbitt, C. C. Silverman, J. S. Powell, K. Mortensen, H. M. Hirbo, J. B. Osman, M. Ibrahim, M. Omar, S. A. Lema, G. Nyambo, T. B. Ghori, J. Bumpstead, S. Pritchard, J. K. Wray, G. A. Deloukas, P.2007GConvergent adaptation of human lactase persistence in Africa and Europe31-40 Nat Genet391*Adaptation, Biological Adult Africa Animals Caco-2 Cells Europe Evolution, Molecular Gene Frequency Haplotypes Humans Lactase/*genetics Lactose/blood/*metabolism Lactose Tolerance Test Milk/metabolism Polymorphism, Single Nucleotide Selection (Genetics)JanA SNP in the gene encoding lactase (LCT) (C/T-13910) is associated with the ability to digest milk as adults (lactase persistence) in Europeans, but the genetic basis of lactase persistence in Africans was previously unknown. We conducted a genotype-phenotype association study in 470 Tanzanians, Kenyans and Sudanese and identified three SNPs (G/C-14010, T/G-13915 and C/G-13907) that are associated with lactase persistence and that have derived alleles that significantly enhance transcription from the LCT promoter in vitro. These SNPs originated on different haplotype backgrounds from the European C/T-13910 SNP and from each other. Genotyping across a 3-Mb region demonstrated haplotype homozygosity extending >2.0 Mb on chromosomes carrying C-14010, consistent with a selective sweep over the past approximately 7,000 years. These data provide a marked example of convergent evolution due to strong selective pressure resulting from shared cultural traits-animal domestication and adult milk consumption.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17159977 1061-4036 (Print) Comparative Study Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.17159977bDepartment of Biology, University of Maryland, College Park, Maryland 20742, USA. Tishkoff@umd.edu !~?<Trikalinos, T. A. Salanti, G. Khoury, M. J. Ioannidis, J. P.2006iImpact of violations and deviations in Hardy-Weinberg equilibrium on postulated gene-disease associations300-9Am J Epidemiol1634Bias (Epidemiology) Databases, Genetic *Genetic Heterogeneity Genetic Markers/*genetics Genetic Predisposition to Disease/epidemiology/*genetics Humans *Models, Statistical Polymorphism, Genetic/*genetics Reproducibility of ResultsFeb 15The authors evaluated whether statistically significant violations of Hardy-Weinberg equilibrium (HWE) or the magnitude of deviations from HWE may contribute to the problem of replicating postulated gene-disease associations across different studies. Forty-two gene-disease associations assessed in meta-analyses of 591 studies were examined. Studies with disease-free controls in which HWE was violated gave significantly different results from HWE-conforming studies in five instances. Exclusion of the former studies resulted in loss of statistical significance of the overall meta-analysis in three instances and more than a 10% change in the summary odds ratio in six. Exclusion of HWE-violating studies changed the formal significance of the estimated between-study heterogeneity in three instances. After adjustment for the magnitude of the deviation from HWE for the controls, formal significance was lost in another three instances. Studies adjusted for the magnitude of deviation from HWE tended to become more heterogeneous among themselves, and, for seven gene-disease associations, between-study heterogeneity became significant, while it was not so in the unadjusted analyses. Gene-disease association studies and meta-analyses thereof should routinely scrutinize the potential impact of HWE violations as well as nonsignificant deviations from the exact frequencies expected under HWE. Postulated genetic associations with modest-sized odds ratios and borderline statistical significance may not be robust in such sensitivity analyses.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16410351 /0002-9262 (Print) Journal Article Meta-Analysis16410351Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina 45100, Greece.~?/Tsai, S. J. Wang, Y. C. Chen, J. Y. Hong, C. J.2003pAllelic variants of the tryptophan hydroxylase (A218C) and serotonin 1B receptor (A-161T) and personality traits68-71Neuropsychobiology482;Adult Alanine/genetics *Alleles Cysteine/genetics Female Genotype Humans Male Middle Aged Personality/*genetics Polymerase Chain Reaction/methods Polymorphism, Genetic Questionnaires Receptor, Serotonin, 5-HT1B Receptors, Serotonin/*genetics Threonine/genetics Tryptophan Hydroxylase/*genetics *Variation (Genetics)SHuman personality traits have a considerable hereditary component, and central serotonergic activity is implicated in the personality factors of the Tridimensional Personality Questionnaire (TPQ). Our population-based association study tested the hypothesis that the tryptophan hydroxylase (TPH) A218C and serotonin 1B receptor (HTR1B) A-161T polymorphisms were associated with TPQ personality trait scores in a sample population of 209 young healthy Chinese. No significant differences were demonstrated comparing scores of subjects bearing different TPH or HTR1B genotypes; however, a trend for difference in the novelty seeking score comparing TPH genotype groups was determined for the male population. Our negative findings suggest that the TPH A218C and HTR1B polymorphisms do not play major roles in the determination of TPQ personality traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14504413 T0302-282X (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't14504413eDepartment of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan, ROC. sjtsai@vghtpe.gov.tw~?#Tsai, S. J. Wang, Y. C. Hong, C. J.2001xAllelic variants of the alpha1a adrenoceptor and the promoter region of the alpha2a adrenoceptor and temperament factors96-8Am J Med Genet1051Adult *Alleles Female Genotype Humans Male *Personality Polymorphism, Genetic Promoter Regions (Genetics)/*genetics Receptors, Adrenergic/*geneticsJan 8xHuman personality traits are partially determined by genes. It has been suggested that the reward-dependence dimension assessed by the Tridimensional Personality Questionnaire (TPQ) is related to the central noradrenergic system. Our population-based association study tested the hypothesis that genetic variants of the adrenoceptor are associated with this personality trait. The alpha1a- and the alpha2a-adrenoceptor genotypes were determined for 198 healthy Han Chinese who had completed the TPQ. We found no significant differences for TPQ personality-factor scores, including reward dependence and its subscales, for subjects showing different adrenoceptor genotypes. Our negative findings suggest that polymorphisms of the alpha1a adrenoceptor and of the promoter region of the alpha2a-adrenoceptor have no major effect on the reward-dependence personality trait as assessed by TPQ.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11425010 B0148-7299 (Print) Journal Article Research Support, Non-U.S. Gov't11425010kDepartment of Psychiatry, Veterans General Hospital-Taipei, Taiwan, Republic of China. sjtsai@vghtpe.gov.tw~?Tu, I. P. Whittemore, A. S.1999NPower of association and linkage tests when the disease alleles are unobserved641-9Am J Hum Genet642}*Alleles Gene Frequency Genetic Markers Humans *Linkage (Genetics) Linkage Disequilibrium Models, Genetic Models, StatisticalFebGenomewide association studies have been advocated as a promising alternative to genomewide linkage scans for detection of small-effect genes in complex diseases. Comparisons of power and sample size between the two strategies have shown considerable advantages for the association studies. These comparisons assume that the set of markers includes the exact disease-related polymorphism. A concern, however, is that the power of an association study decreases when this is not the case, because of discrepant allele frequencies and less-than-maximum disequilibrium between the disease-related polymorphism and its nearest marker. Here, we quantify this concern by comparing the sample sizes needed by the two strategies when the markers exclude the disease-related polymorphism. For affected sib pairs and their parents, we found that incomplete disequilibrium and differing allele frequencies can have substantial negative impact on the power of association studies, resulting, in some circumstances, in little gain and even in loss of power, compared with linkage analysis. We provide some guidelines for choosing between strategies, for the detection of genes for complex diseases.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9973303 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9973303dDepartment of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA.V? Tukey, J.W.1951Components in regression.33-69 Biometrics 7~?Turkheimer, E. Gottesman, I.I. 1991$Is H2 = 0 a null hypothesis anymore?410-411Behav. Brain Sci.14~?ATurkheimer, E. Haley, A. Waldron, M. D'Onofrio, B. Gottesman,, II2003BSocioeconomic status modifies heritability of IQ in young children623-8 Psychol Sci146Biometry/methods Child, Preschool Cognition Disorders/epidemiology/*genetics Follow-Up Studies Humans *Intelligence Socioeconomic Factors United States/epidemiologyNovTScores on the Wechsler Intelligence Scale for Children were analyzed in a sample of 7-year-old twins from the National Collaborative Perinatal Project. A substantial proportion of the twins were raised in families living near or below the poverty level. Biometric analyses were conducted using models allowing for components attributable to the additive effects of genotype, shared environment, and nonshared environment to interact with socioeconomic status (SES) measured as a continuous variable. Results demonstrate that the proportions of IQ variance attributable to genes and environment vary nonlinearly with SES. The models suggest that in impoverished families, 60% of the variance in IQ is accounted for by the shared environment, and the contribution of genes is close to zero; in affluent families, the result is almost exactly the reverse.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14629696 !0956-7976 (Print) Journal Article14629696FUniversity of Virginia, Charlottesville 22904, USA. ent3c@virginia.edu~?0Tzeng, J. Y. Wang, C. H. Kao, J. T. Hsiao, C. K.2006XRegression-Based Association Analysis with Clustered Haplotypes through Use of Genotypes231-42Am J Hum Genet782FebHaplotype-based association analysis has been recognized as a tool with high resolution and potentially great power for identifying modest etiological effects of genes. However, in practice, its efficacy has not been as successfully reproduced as expected in theory. One primary cause is that such analysis tends to require a large number of parameters to capture the abundant haplotype varieties, and many of those are expended on rare haplotypes for which studies would have insufficient power to detect association even if it existed. To concentrate statistical power on more-relevant inferences, in this study, we developed a regression-based approach using clustered haplotypes to assess haplotype-phenotype association. Specifically, we generalized the probabilistic clustering methods of Tzeng to the generalized linear model (GLM) framework established by Schaid et al. The proposed method uses unphased genotypes and incorporates both phase uncertainty and clustering uncertainty. Its GLM framework allows adjustment of covariates and can model qualitative and quantitative traits. It can also evaluate the overall haplotype association or the individual haplotype effects. We applied the proposed approach to study the association between hypertriglyceridemia and the apolipoprotein A5 gene. Through simulation studies, we assessed the performance of the proposed approach and demonstrate its validity and power in testing for haplotype-trait association.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16365833 !0002-9297 (Print) Journal Article16365833Department of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA. jytzeng@stat.ncsu.edu.&~?QUimari, P. Kontkanen, O. Visscher, P. M. Pirskanen, M. Fuentes, R. Salonen, J. T.2005[Genome-wide linkage disequilibrium from 100,000 SNPs in the East Finland founder population185-97Twin Res Hum Genet83Alleles Case-Control Studies Chi-Square Distribution Chromosomes, Human, X Female Finland Founder Effect Gene Frequency *Genetics, Population *Genome, Human Genotype Humans Linkage Disequilibrium/*genetics Male Polymorphism, Single Nucleotide/*geneticsJun/Information about linkage disequilibrium (LD) is important in understanding the genome structure and has its applications in association studies. Here we present the first genome-wide LD study based on a founder population (East Finland). The LD data consist of 118 unrelated individuals and around 480,000 SNP pairs genotyped with the Affymetrix 100K genotyping assay. Using the minor allele frequency (MAF) limit of .05, the squared correlation coefficient between two loci (r(2)) was .48, .37, .28, and .20 for distances of 5, 10, 20, and 40 kb respectively. MAF had a significant effect on the mean r(2) so that the extent of useful LD (r(2) > .3) varied from 17 kb to 80 kb depending on the limit set for the MAF. For D' the effect of MAF was smaller but reflected the possible age of the mutation: SNPs with high MAF had lower D' than those with low MAF. The X chromosome showed higher D' values than autosomes and the extent of useful LD (r(2) > .3) was twice as long on the X chromosome than on the autosomes. Based on the results, LD varies across the genome and is correlated to local recombination rate between and within chromosomes. However, the recombination rate does not explain all the variation found in LD. We also report a number of long chromosomal regions where exceptionally high or low LD were detected.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15989746 B1832-4274 (Print) Journal Article Research Support, Non-U.S. Gov't15989746IJurilab Ltd, Microkatu 1, 70210 Kuopio, Finland. pekka.uimari@jurilab.com~?+van den Berg, S. M. Beem, L. Boomsma, D. I.2006JFitting genetic models using Markov Chain Monte Carlo algorithms with BUGS334-42Twin Res Hum Genet93Algorithms Bayes Theorem Follicle Stimulating Hormone/urine Humans Luteinizing Hormone/urine *Markov Chains *Models, Genetic Models, Statistical *Monte Carlo Method *Software *Twin Studies Twins, Dizygotic/genetics Twins, Monozygotic/geneticsJunMaximum likelihood estimation techniques are widely used in twin and family studies, but soon reach computational boundaries when applied to highly complex models (e.g., models including gene-by-environment interaction and gene-environment correlation, item response theory measurement models, repeated measures, longitudinal structures, extended pedigrees). Markov Chain Monte Carlo (MCMC) algorithms are very well suited to fit complex models with hierarchically structured data. This article introduces the key concepts of Bayesian inference and MCMC parameter estimation and provides a number of scripts describing relatively simple models to be estimated by the freely obtainable BUGS software. In addition, inference using BUGS is illustrated using a data set on follicle-stimulating hormone and luteinizing hormone levels with repeated measures. The examples provided can serve as stepping stones for more complicated models, tailored to the specific needs of the individual researcher.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16790144 B1832-4274 (Print) Journal Article Research Support, Non-U.S. Gov't16790144oDepartment of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands, SM.van.den.Berg@psy.vu.nl.&~?avan den Berg, S. M. Setiawan, A. Bartels, M. Polderman, T. J. van der Vaart, A. W. Boomsma, D. I.2006pIndividual differences in puberty onset in girls: Bayesian estimation of heritabilities and genetic correlations261-70 Behav Genet362*Bayes Theorem Child Cohort Studies Female Humans *Individuality Longitudinal Studies Menarche/genetics Monte Carlo Method Phenotype Puberty/*genetics Sexual Maturation/genetics Social Environment Statistics Twins, Dizygotic/*genetics Twins, Monozygotic/*geneticsMar: We report heritabilities for individual differences in female pubertal development at the age of 12. Tanner data on breast and pubic hair development in girls and data on menarche were obtained from a total of 184 pairs of monozygotic and dizygotic twins. Genetic correlations were estimated to determine to what extent the same genes are involved in different aspects of physical development in puberty. A Bayesian estimation approach was taken, using Markov-chain Monte Carlo simulation to estimate model parameters. All three phenotypes were to a significant extent heritable and showed high genetic correlations, suggesting that a common set of genes is involved in the timing of puberty in general. However, gonadarche (menarche and breast development) and adrenarche (pubic hair) are affected by different environmental factors, which does not support the three phenotypes to be regarded as indicators of a unitary physiological factor.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16408250 M0001-8244 (Print) Journal Article Research Support, Non-U.S. Gov't Twin Study16408250Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 1, Amsterdam, The Netherlands. SM.van.den.Berg@psy.vu.nl~? van den Oord, E. J. Neale, B. M.20040Will haplotype maps be useful for finding genes?227-36Mol Psychiatry93lChromosome Mapping/*methods *Genes *Haplotypes Humans Linkage Disequilibrium Polymorphism, Single NucleotideMarFrom its introduction into the literature, the idea of haplotype map-based linkage disequilibrium (LD) studies has been the subject of disputes. These queries involve the extent to which the haplotype blocks exist, the validity of fundamental concepts such as the recombination hotspot, and the application of this idea in the form of the HapMap project. In this article, we review the relevant literature to evaluate the potential importance of haplotype maps for psychiatric genetics. We first take a closer look at the nature of haplotype blocks and then address the impact of block definitions and methodological factors, such as single-nucleotide polymorphism density and sample size, on findings from haplotype block studies. After distinguishing between two types of haplotype map-based LD studies, we discuss the importance of the recombination hotspot and the nature of the disease mutations affecting complex traits. In the final section, we summarize our main conclusions and comment on the usefulness of haplotype maps for finding genes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14610524 n1359-4184 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Review14610524Virginia Institute for Psychiatric and Behavioral Genetics, Medical College of Virginia of Virginia Commonwealth University, Richmond, VA, USA. ejvandenoord@vcu.edu(~?Nvan den Oord, E. J. Simonoff, E. Eaves, L. J. Pickles, A. Silberg, J. Maes, H.2000cAn evaluation of different approaches for behavior genetic analyses with psychiatric symptom scores1-18 Behav Genet301Adolescent Adult Child Conduct Disorder/diagnosis/*genetics Diseases in Twins/*genetics Genetics, Behavioral Humans Male *Models, Genetic Personality Assessment/*statistics & numerical data PsychometricsJanmWe used a simulation study to evaluate six approaches for behavior genetic analyses of psychiatric symptom scores. For the selection of the correct model, the best results were obtained with approaches using transformed scores in combination with a procedure involving p-values. With normalizing transformations, the chi 2 test statistic gave a reasonable impression of the overall fit of the model but was less accurate when used as a difference test. The asymptotic distribution free estimation methods yielded chi 2s that were much too large. All data analysis techniques yielded substantially biased parameter estimates. The most biased results were obtained with normalizing transformations. The least biased results were obtained with tobit correlations, but because of its large standard errors the most precise estimates were obtained with polychoric correlations and optimal scale scores. An empirical study showed that a recognition of the role of methodological factors was helpful to understand part of the differences between assessment instruments, raters, and data analysis techniques that were found in the real data.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10934795 ,0001-8244 (Print) Journal Article Twin Study10934795WMRC Child Psychiatry Unit, Institute of Psychiatry, London, U.K. E.vandenOord@fss.uu.nl?van den Oord, E.J.C.G1999WA comparison between different designs and tests to detects QTLs in association studies245-256 Behav. Genet.29L~?Vandenberg, S. G.1965;Innate Abilities, One or Many?a New Method and Some Results41-7Acta Genet Med Gemellol (Roma)14*Intelligence Tests *TwinsJanfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14307213 !0001-5660 (Print) Journal Article14307213?Vandenberg, S.G1965*Multivariate analysis of twin differences.29-44,Methods and Goals in Human Behavior GeneticsS.G. Vandenberg~?Varmuza, S. Mann, M.19942Genomic imprinting--defusing the ovarian time bomb118-23 Trends Genet104*Alleles Animals Choriocarcinoma/*genetics Embryonic and Fetal Development/*genetics Female Gene Expression Regulation/*physiology Humans Male Mammals/genetics Methylation Mice *Models, Biological Oocytes/pathology Ovarian Neoplasms/*genetics *Parents *Parthenogenesis Phenotype Pregnancy Selection (Genetics) Teratoma/*genetics Testicular Neoplasms/genetics Trophoblastic Neoplasms/*genetics Uterine Neoplasms/*geneticsAprWhy do mammals imprint their parental genomes? Imprinting is seen in many phyla, but that in mammals is by far the most dramatic. Is there something peculiar to mammals that calls for such a striking phenomenon? We propose that imprinting is a device that protects female mammals from the potential ravages of ovarian trophoblast disease. Without imprinting, the ovarian teratomas that frequently arise from parthenogenetically activated oocytes in situ might be capable of forming malignant trophoblast. An allele that favored imprinting would spread rapidly because of the great increase in fitness associated with suppressing a lethal cancer of females.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7848407 !0168-9525 (Print) Journal Article7848407>Department of Zoology, University of Toronto, Ontario, Canada.?7Venables, W.N. Smith, D.M. the R Development Core Team,2001An Introduction to R.BristolNetwork Theory Limited~?" Venter, J. C. Adams, M. D. Myers, E. W. Li, P. W. Mural, R. J. Sutton, G. G. Smith, H. O. Yandell, M. Evans, C. A. Holt, R. A. Gocayne, J. D. Amanatides, P. Ballew, R. M. Huson, D. H. Wortman, J. R. Zhang, Q. Kodira, C. D. Zheng, X. H. Chen, L. Skupski, M. Subramanian, G. Thomas, P. D. Zhang, J. Gabor Miklos, G. L. Nelson, C. Broder, S. Clark, A. G. Nadeau, J. McKusick, V. A. Zinder, N. Levine, A. J. Roberts, R. J. Simon, M. Slayman, C. Hunkapiller, M. Bolanos, R. Delcher, A. Dew, I. Fasulo, D. Flanigan, M. Florea, L. Halpern, A. Hannenhalli, S. Kravitz, S. Levy, S. Mobarry, C. Reinert, K. Remington, K. Abu-Threideh, J. Beasley, E. Biddick, K. Bonazzi, V. Brandon, R. Cargill, M. Chandramouliswaran, I. Charlab, R. Chaturvedi, K. Deng, Z. Di Francesco, V. Dunn, P. Eilbeck, K. Evangelista, C. Gabrielian, A. E. Gan, W. Ge, W. Gong, F. Gu, Z. Guan, P. Heiman, T. J. Higgins, M. E. Ji, R. R. Ke, Z. Ketchum, K. A. Lai, Z. Lei, Y. Li, Z. Li, J. Liang, Y. Lin, X. Lu, F. Merkulov, G. V. Milshina, N. Moore, H. M. Naik, A. K. Narayan, V. A. Neelam, B. Nusskern, D. Rusch, D. B. Salzberg, S. Shao, W. Shue, B. Sun, J. Wang, Z. Wang, A. Wang, X. Wang, J. Wei, M. Wides, R. Xiao, C. Yan, C. Yao, A. Ye, J. Zhan, M. Zhang, W. Zhang, H. Zhao, Q. Zheng, L. Zhong, F. Zhong, W. Zhu, S. Zhao, S. Gilbert, D. Baumhueter, S. Spier, G. Carter, C. Cravchik, A. Woodage, T. Ali, F. An, H. Awe, A. Baldwin, D. Baden, H. Barnstead, M. Barrow, I. Beeson, K. Busam, D. Carver, A. Center, A. Cheng, M. L. Curry, L. Danaher, S. Davenport, L. Desilets, R. Dietz, S. Dodson, K. Doup, L. Ferriera, S. Garg, N. Gluecksmann, A. Hart, B. Haynes, J. Haynes, C. Heiner, C. Hladun, S. Hostin, D. Houck, J. Howland, T. Ibegwam, C. Johnson, J. Kalush, F. Kline, L. Koduru, S. Love, A. Mann, F. May, D. McCawley, S. McIntosh, T. McMullen, I. Moy, M. Moy, L. Murphy, B. Nelson, K. Pfannkoch, C. Pratts, E. Puri, V. Qureshi, H. Reardon, M. Rodriguez, R. Rogers, Y. H. Romblad, D. Ruhfel, B. Scott, R. Sitter, C. Smallwood, M. Stewart, E. Strong, R. Suh, E. Thomas, R. Tint, N. N. Tse, S. Vech, C. Wang, G. Wetter, J. Williams, S. Williams, M. Windsor, S. Winn-Deen, E. Wolfe, K. Zaveri, J. Zaveri, K. Abril, J. F. Guigo, R. Campbell, M. J. Sjolander, K. V. Karlak, B. Kejariwal, A. Mi, H. Lazareva, B. Hatton, T. Narechania, A. Diemer, K. Muruganujan, A. Guo, N. Sato, S. Bafna, V. Istrail, S. Lippert, R. Schwartz, R. Walenz, B. Yooseph, S. Allen, D. Basu, A. Baxendale, J. Blick, L. Caminha, M. Carnes-Stine, J. Caulk, P. Chiang, Y. H. Coyne, M. Dahlke, C. Mays, A. Dombroski, M. Donnelly, M. Ely, D. Esparham, S. Fosler, C. Gire, H. Glanowski, S. Glasser, K. Glodek, A. Gorokhov, M. Graham, K. Gropman, B. Harris, M. Heil, J. Henderson, S. Hoover, J. Jennings, D. Jordan, C. Jordan, J. Kasha, J. Kagan, L. Kraft, C. Levitsky, A. Lewis, M. Liu, X. Lopez, J. Ma, D. Majoros, W. McDaniel, J. Murphy, S. Newman, M. Nguyen, T. Nguyen, N. Nodell, M. Pan, S. Peck, J. Peterson, M. Rowe, W. Sanders, R. Scott, J. Simpson, M. Smith, T. Sprague, A. Stockwell, T. Turner, R. Venter, E. Wang, M. Wen, M. Wu, D. Wu, M. Xia, A. Zandieh, A. Zhu, X.2001 The sequence of the human genome1304-51Science2915507Algorithms Animals Chromosome Banding Chromosome Mapping Chromosomes, Artificial, Bacterial Computational Biology Consensus Sequence CpG Islands DNA, Intergenic Databases, Factual Evolution, Molecular Exons Female Gene Duplication Genes *Genome, Human *Human Genome Project Humans Introns Male Phenotype Physical Chromosome Mapping Polymorphism, Single Nucleotide Proteins/genetics/physiology Pseudogenes Repetitive Sequences, Nucleic Acid Retroelements *Sequence Analysis, DNA/methods Species Specificity Variation (Genetics)Feb 16_ A 2.91-billion base pair (bp) consensus sequence of the euchromatic portion of the human genome was generated by the whole-genome shotgun sequencing method. The 14.8-billion bp DNA sequence was generated over 9 months from 27,271,853 high-quality sequence reads (5.11-fold coverage of the genome) from both ends of plasmid clones made from the DNA of five individuals. Two assembly strategies-a whole-genome assembly and a regional chromosome assembly-were used, each combining sequence data from Celera and the publicly funded genome effort. The public data were shredded into 550-bp segments to create a 2.9-fold coverage of those genome regions that had been sequenced, without including biases inherent in the cloning and assembly procedure used by the publicly funded group. This brought the effective coverage in the assemblies to eightfold, reducing the number and size of gaps in the final assembly over what would be obtained with 5.11-fold coverage. The two assembly strategies yielded very similar results that largely agree with independent mapping data. The assemblies effectively cover the euchromatic regions of the human chromosomes. More than 90% of the genome is in scaffold assemblies of 100,000 bp or more, and 25% of the genome is in scaffolds of 10 million bp or larger. Analysis of the genome sequence revealed 26,588 protein-encoding transcripts for which there was strong corroborating evidence and an additional approximately 12,000 computationally derived genes with mouse matches or other weak supporting evidence. Although gene-dense clusters are obvious, almost half the genes are dispersed in low G+C sequence separated by large tracts of apparently noncoding sequence. Only 1.1% of the genome is spanned by exons, whereas 24% is in introns, with 75% of the genome being intergenic DNA. Duplications of segmental blocks, ranging in size up to chromosomal lengths, are abundant throughout the genome and reveal a complex evolutionary history. Comparative genomic analysis indicates vertebrate expansions of genes associated with neuronal function, with tissue-specific developmental regulation, and with the hemostasis and immune systems. DNA sequence comparisons between the consensus sequence and publicly funded genome data provided locations of 2.1 million single-nucleotide polymorphisms (SNPs). A random pair of human haploid genomes differed at a rate of 1 bp per 1250 on average, but there was marked heterogeneity in the level of polymorphism across the genome. Less than 1% of all SNPs resulted in variation in proteins, but the task of determining which SNPs have functional consequences remains an open challenge.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11181995 B0036-8075 (Print) Journal Article Research Support, Non-U.S. Gov't11181995UCelera Genomics, 45 West Gude Drive, Rockville, MD 20850, USA. humangenome@celera.com!~?,Vieland, V. J. Hodge, S. E. Greenberg, D. A.1992QAdequacy of single-locus approximations for linkage analyses of oligogenic traits45-59Genet Epidemiol91Chromosome Mapping/methods *Computer Simulation Genetic Diseases, Inborn/epidemiology Humans Linkage (Genetics)/*genetics Lod Score *Models, Genetic Prevalence Recombination, Genetic/geneticsWhen a disease is controlled by two or more mendelian loci acting epistatically, it can be modeled in a linkage analysis as a single-locus mendelian disease with reduced penetrance. However, the reliability of such an approximation has not yet been demonstrated. This study evaluates the adequacy of such single-locus approximations, when the disease under investigation is determined by two loci, one of which is tightly linked to a genetic marker. A wide range of two-locus models were simulated, and analyzed under both the correct two-locus model and under a single-locus approximation to that model. In general, the single-locus approximations yielded lod scores very close to the correct ones, but estimates of theta tended to be upwardly biased. We conclude that a single-locus linkage analysis will, in general, provide an excellent approximation to a correct (two-locus) linkage analysis of epistatic two-locus diseases. This enables researchers to continue to use single-locus linkage analyses when two-locus disease transmission is a possibility, and it validates linkage findings already obtained under single-locus analysis, even if the disease under investigation proves ultimately to be governed by two mendelian loci. We also examine alternative methods for obtaining parameter estimates for the single-locus approximations, and we discuss both generalizations and limitations of our findings.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1634106 g0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.1634106EDepartment of Biostatistics, Columbia University, New York, New York.~?Visscher, P. M.2006[A note on the asymptotic distribution of likelihood ratio tests to test variance components490-5Twin Res Hum Genet94Chromosome Mapping/methods Female Humans Male *Models, Genetic Quantitative Trait Loci/*genetics Twins/*genetics *Variation (Genetics)AugNWhen using maximum likelihood methods to estimate genetic and environmental components of (co)variance, it is common to test hypotheses using likelihood ratio tests, since such tests have desirable asymptotic properties. In particular, the standard likelihood ratio test statistic is assumed asymptotically to follow a chi2 distribution with degrees of freedom equal to the number of parameters tested. Using the relationship between least squares and maximum likelihood estimators for balanced designs, it is shown why the asymptotic distribution of the likelihood ratio test for variance components does not follow a chi2 distribution with degrees of freedom equal to the number of parameters tested when the null hypothesis is true. Instead, the distribution of the likelihood ratio test is a mixture of chi2 distributions with different degrees of freedom. Implications for testing variance components in twin designs and for quantitative trait loci mapping are discussed. The appropriate distribution of the likelihood ratio test statistic should be used in hypothesis testing and model selection.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16899155 !1832-4274 (Print) Journal Article16899155Queensland Institute of Medical Research, Brisbane, Australia; Institute of Evolutionary Biology, University of Edinburgh, United Kingdom. peter.visscher@qimr.edu.au~?Visscher, P. M. Duffy, D. L.2006kThe value of relatives with phenotypes but missing genotypes in association studies for quantitative traits30-6Genet Epidemiol301Genetic Predisposition to Disease/*genetics *Genotype Humans Models, Genetic *Phenotype Polymorphism, Single Nucleotide/genetics Quantitative Trait Loci Regression Analysis Twins, Monozygotic/geneticsJanThe additional statistical power of association studies for quantitative traits was derived when ungenotyped relatives with phenotypes are included in the analysis. It was shown that the extra power is a simple function of the coefficient of additive genetic relationship and the phenotypic correlation coefficient between the genotyped and ungenotyped relatives. For close relatives, such as pairs of fullsibs and identical twin pairs, gains in power in the range of 10 to 30% are achieved if only one of the pair is genotyped. The theoretical results were verified by simulations. It was shown that ignoring the error in estimating the genotype of the ungenotyped relative has little impact on the estimates and on statistical power, consistent with results from quantitative trait loci (QTL) linkage studies. For genome-wide association studies in which not all relatives with phenotypes can be genotyped, our study provides a prediction of the additional power of an analysis that includes phenotypes on ungenotyped individuals, and can be used in experimental design. We show that a two-step procedure, in which missing genotypes are imputed and subsequently an association analysis is performed, is efficient and powerful.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16355405 g0741-0395 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16355405oGenetic Epidemiology, Queensland Institute of Medical Research, Brisbane, Australia. peter.visscher@qimr.edu.au i~?Visscher, P. M. Hopper, J. L.2001\Power of regression and maximum likelihood methods to map QTL from sib-pair and DZ twin data583-601 Ann Hum Genet65Pt 6*Chromosome Mapping Computer Simulation *Likelihood Functions Models, Genetic Models, Statistical Nuclear Family *Quantitative Trait, Heritable *Regression Analysis Research Design *Twin Studies/methods Twins, Dizygotic/genetics Twins, Monozygotic/geneticsNovA common study design to map quantitative trait loci (QTL) is to compare the phenotypes and marker genotypes of two or more siblings in a sample of unrelated sib groups, and to test for linkage between chromosome location and quantitative trait values. The simplest case is sib pairs only, in particular dizygotic twin pairs, and a simple and elegant regression method was proposed by Haseman & Elston in 1972 to test for linkage. Since then, several other methods have been proposed to test for linkage. In this study, we derived the statistical power of linear regression and maximum likelihood methods to map QTL from sib pair data analytically, and determined which methods are superior under which set of population parameters. In particular, we considered four regression-based and three maximum likelihood-based approaches, and derived asymptotic approximations of the mean test statistic and statistical power for each method. It was found, both analytically and by computer simulation, that the revisited or new Haseman-Elston method (based upon the mean-corrected crossproduct of the observations on sib-pairs) is less powerful than a full maximum likelihood approach and is also inferior to the Haseman-Elston method under a realistic range of values for the population parameters. We found that a simple regression method, based upon both the squared difference and the mean-corrected squared sum of the observations on sib-pairs, is as powerful as a full maximum likelihood approach. Our derivations of statistical power for regression and maximum likelihood methods provide a simple way to compare alternative methods and obviate the need to perform elaborate computer simulations. DZ twin pairs are likely to be more powerful for linkage analysis than ordinary siblings because they may share more common environmental effects, thereby increasing the proportion of within-family variance that is explained by a QTL.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11851988 B0003-4800 (Print) Journal Article Research Support, Non-U.S. Gov't11851988`Centre for Genetic Epidemiology, The University of Melbourne, Australia. peter.visscher@ed.ac.uk ~?rVisscher, P. M. Medland, S. E. Ferreira, M. A. Morley, K. I. Zhu, G. Cornes, B. K. Montgomery, G. W. Martin, N. G.2006mAssumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblingse41 PLoS Genet23*Body Height Chromosome Mapping Family Health Genetic Markers Genome Humans Likelihood Functions *Models, Genetic Models, Statistical Phenotype Siblings Variation (Genetics)MarThe study of continuously varying, quantitative traits is important in evolutionary biology, agriculture, and medicine. Variation in such traits is attributable to many, possibly interacting, genes whose expression may be sensitive to the environment, which makes their dissection into underlying causative factors difficult. An important population parameter for quantitative traits is heritability, the proportion of total variance that is due to genetic factors. Response to artificial and natural selection and the degree of resemblance between relatives are all a function of this parameter. Following the classic paper by R. A. Fisher in 1918, the estimation of additive and dominance genetic variance and heritability in populations is based upon the expected proportion of genes shared between different types of relatives, and explicit, often controversial and untestable models of genetic and non-genetic causes of family resemblance. With genome-wide coverage of genetic markers it is now possible to estimate such parameters solely within families using the actual degree of identity-by-descent sharing between relatives. Using genome scans on 4,401 quasi-independent sib pairs of which 3,375 pairs had phenotypes, we estimated the heritability of height from empirical genome-wide identity-by-descent sharing, which varied from 0.374 to 0.617 (mean 0.498, standard deviation 0.036). The variance in identity-by-descent sharing per chromosome and per genome was consistent with theory. The maximum likelihood estimate of the heritability for height was 0.80 with no evidence for non-genetic causes of sib resemblance, consistent with results from independent twin and family studies but using an entirely separate source of information. Our application shows that it is feasible to estimate genetic variance solely from within-family segregation and provides an independent validation of previously untestable assumptions. Given sufficient data, our new paradigm will allow the estimation of genetic variation for disease susceptibility and quantitative traits that is free from confounding with non-genetic factors and will allow partitioning of genetic variation into additive and non-additive components.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16565746 l1553-7404 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't16565746uGenetic Epidemiology Group, Queensland Institute of Medical Research, Brisbane, Australia. peter.visscher@qimr.edu.au~?`Vogler, G. P. Tang, W. Nelson, T. L. Hofer, S. M. Grant, J. D. Tarantino, L. M. Fernandez, J. R.1997A multivariate model for the analysis of sibship covariance structure using marker information and multiple quantitative traits921-6Genet Epidemiol146Alleles Analysis of Variance Chromosome Mapping *Computer Simulation Female *Genetic Markers Humans Likelihood Functions Male Matched-Pair Analysis *Models, Statistical Multivariate Analysis *Nuclear Family Phenotype *Quantitative Trait, HeritableA model was developed to detect effects of quantitative trait loci (QTLs) in sibships from simulated nuclear family data using the full covariance structure of the data and analyzing all five quantitative traits simultaneously in a multivariate model. Evidence of the presence of loci was detected on chromosomes 4, 8, 9, and 10. The method provided stable results and is worth further exploration for its performance and optimal sample size requirements under realistic conditions.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9433601 F0741-0395 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.9433601^Department of Biobehavioral Health, Pennsylvania State University, University Park 16802, USA.~?Wall, J. D. Pritchard, J. K.2003PAssessing the performance of the haplotype block model of linkage disequilibrium502-15Am J Hum Genet733_Computer Simulation *Haplotypes *Linkage Disequilibrium *Models, Genetic Recombination, GeneticSeprSeveral recent studies have suggested that linkage disequilibrium (LD) in the human genome has a fundamentally "blocklike" structure. However, thus far there has been little formal assessment of how well the haplotype block model captures the underlying structure of LD. Here we propose quantitative criteria for assessing how blocklike LD is and apply these criteria to both real and simulated data. Analyses of several large data sets indicate that real data show a partial fit to the haplotype block model; some regions conform quite well, whereas others do not. Some improvement could be obtained by genotyping higher marker densities but not by increasing the number of samples. Nonetheless, although the real data are only moderately blocklike, our simulations indicate that, under a model of uniform recombination, the structure of LD would actually fit the block model much less well. Simulations of a model in which much of the recombination occurs in narrow hotspots provide a much better fit to the observed patterns of LD, suggesting that there is extensive fine-scale variation in recombination rates across the human genome.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12916017 Y0002-9297 (Print) Evaluation Studies Journal Article Research Support, U.S. Gov't, P.H.S.12916017aDepartment of Human Genetics, The University of Chicago, Chicago, IL 60637, USA. jeffwall@usc.edu B~?Wallace, C. Xue, M. Z. Newhouse, S. J. Marcano, A. C. Onipinla, A. K. Burke, B. Gungadoo, J. Dobson, R. J. Brown, M. Connell, J. M. Dominiczak, A. Lathrop, G. M. Webster, J. Farrall, M. Mein, C. Samani, N. J. Caulfield, M. J. Clayton, D. G. Munroe, P. B.2006uLinkage analysis using co-phenotypes in the BRIGHT study reveals novel potential susceptibility loci for hypertension323-31Am J Hum Genet792Chromosome Mapping Female Genetic Markers *Genetic Predisposition to Disease Genome, Human Great Britain Humans Hypertension/drug therapy/*genetics *Linkage (Genetics) Male Middle Aged *PhenotypeAug2Identification of the genetic influences on human essential hypertension and other complex diseases has proved difficult, partly because of genetic heterogeneity. In many complex-trait resources, additional phenotypic data have been collected, allowing comorbid intermediary phenotypes to be used to characterize more genetically homogeneous subsets. The traditional approach to analyzing covariate-defined subsets has typically depended on researchers' previous expectations for definition of a comorbid subset and leads to smaller data sets, with a concomitant attrition in power. An alternative is to test for dependence between genetic sharing and covariates across the entire data set. This approach offers the advantage of exploiting the full data set and could be widely applied to complex-trait genome scans. However, existing maximum-likelihood methods can be prohibitively computationally expensive, especially since permutation is often required to determine significance. We developed a less computationally intensive score test and applied it to biometric and biochemical covariate data, from 2,044 sibling pairs with severe hypertension, collected by the British Genetics of Hypertension (BRIGHT) study. We found genomewide-significant evidence for linkage with hypertension and several related covariates. The strongest signals were with leaner-body-mass measures on chromosome 20q (maximum LOD = 4.24) and with parameters of renal function on chromosome 5p (maximum LOD = 3.71). After correction for the multiple traits and genetic locations studied, our global genomewide P value was .046. This is the first identity-by-descent regression analysis of hypertension to our knowledge, and it demonstrates the value of this approach for the incorporation of additional phenotypic information in genetic studies of complex traits.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16826522 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't16826522Clinical Pharmacology and The Genome Centre, The William Harvey Research Institute, Barts and The London, Queen Mary's School of Medicine and Dentistry, Charterhouse Square, London EC1M 6BQ, UK. c.wallace@qmul.ac.uk,~?6Wang, N. Akey, J. M. Zhang, K. Chakraborty, R. Jin, L.2002Distribution of recombination crossovers and the origin of haplotype blocks: the interplay of population history, recombination, and mutation1227-34Am J Hum Genet715Algorithms Computer Simulation *Crossing Over, Genetic *Haplotypes Models, Genetic *Mutation Polymorphism, Single Nucleotide *Recombination, GeneticNovTRecent studies suggest that haplotypes are arranged into discrete blocklike structures throughout the human genome. Here, we present an alternative haplotype block definition that assumes no recombination within each block but allows for recombination between blocks, and we use it to study the combined effects of demographic history and various population genetic parameters on haplotype block characteristics. Through extensive coalescent simulations and analysis of published haplotype data on chromosome 21, we find that (1) the combined effects of population demographic history, recombination, and mutation dictate haplotype block characteristics and (2) haplotype blocks can arise in the absence of recombination hot spots. Finally, we provide practical guidelines for designing and interpreting studies investigating haplotype block structure.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12384857 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.12384857XCenter for Genome Information, University of Cincinnati, Cincinnati, OH 45267-0056, USA.~?Wang, T. Elston, R. C.2005FTwo-level Haseman-Elston regression for general pedigree data analysis12-22Genet Epidemiol291xGenetic Diseases, Inborn/*genetics Humans Linkage (Genetics)/*genetics Models, Statistical *Pedigree Regression AnalysisJulMThe Haseman-Elston (HE) (Haseman and Elston [1972] Behav Genet 2:3-19) method is widely used in genetic linkage studies for quantitative traits. We propose a new version of the HE regression model, a two-level HE regression model (tHE) in which the variance-covariance structure of family data is modeled under the framework of multiple-level regression. An iterative generalized least squares (IGLS) algorithm is adopted to handle the varying variance-covariance structures across families in a simple fashion. In this way, the tHE can compete favorably with any current version of HE in that it can naturally make use of all the trait information available in any general pedigree, simultaneously incorporate individual-level and pedigree-level covariates, marker genotypes for linkage (i.e., the number of allele shared identically by descent [IBD]), and marker alleles for association. Under the assumption of normality, the method is asymptotically equivalent to the usual variance component model for detecting linkage. For the situation where the assumption of normality is critical, a robust globally consistent estimator of the quantitative trait locus (QTL) variance is available. Complex genetic mechanisms, including gene-gene interaction, gene-environmental interaction, and imprinting, can be directly modeled in this version of HE regression.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15838848 0741-0395 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15838848jDepartment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106, USA.P~?Watson, J. D. Crick, F. H.1953NMolecular structure of nucleic acids; a structure for deoxyribose nucleic acid737-8Nature1714356*Nucleic AcidsApr 25fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=13054692 !0028-0836 (Print) Journal Article13054692 $~?jWeale, M. E. Depondt, C. Macdonald, S. J. Smith, A. Lai, P. S. Shorvon, S. D. Wood, N. W. Goldstein, D. B.2003Selection and evaluation of tagging SNPs in the neuronal-sodium-channel gene SCN1A: implications for linkage-disequilibrium gene mapping551-65Am J Hum Genet733Adult Asian Continental Ancestry Group/genetics Child Epilepsy/genetics Female Genetic Techniques Haplotypes Humans *Linkage Disequilibrium Male Nerve Tissue Proteins/*genetics *Polymorphism, Single Nucleotide Singapore Sodium Channels/*geneticsSep Association studies are widely seen as the most promising approach for finding polymorphisms that influence genetically complex traits, such as common diseases and responses to their treatment. Considerable interest has therefore recently focused on the development of methods that efficiently screen genomic regions or whole genomes for gene variants associated with complex phenotypes. One key element in this search is the use of linkage disequilibrium to gain maximal information from typing a selected subset of highly informative single-nucleotide polymorphism (SNP) markers, now often called "tagging SNPs" (tSNPs). Probably the most common approach to linkage-disequilibrium gene mapping involves a three-step program: (1) characterization of the haplotype structure in candidate genes or genomic regions of interest, (2) identification of tSNPs sufficient to represent the most common haplotypes, and (3) typing of tSNPs in clinical material. Early definitions of tSNPs focused on the amount of haplotype diversity that they explained. To select tSNPs that would have maximal power in a genetic association study, however, we have developed optimization criteria based on the r2 measure of association and have compared these with other criteria based on the haplotype diversity. To evaluate the full program and to assess how well the selected tags are likely to perform, we have determined the haplotype structure and have assessed tSNPs in the SCN1A gene, an important candidate gene for sporadic epilepsy. We find that as few as four tSNPs are predicted to maintain a consistently high r2 value with all other common SNPs in the gene, indicating that the tags could be used in an association study with only a modest reduction in power relative to direct assays of all common SNPs. This implies that very large case-control studies can be screened for variation in hundreds of candidate genes with manageable experimental effort, once tSNPs are identified. However, our results also show that tSNPs identified in one population may not necessarily perform well in another, indicating that the preliminary study to identify tSNPs and the later case-control study should be performed in the same population. Our results also indicate that tSNPs will not easily identify discrepant SNPs, which lie on importantly discriminating but apparently short genealogical branches. This could significantly complicate tagging approaches for phenotypes influenced by variants that have experienced positive selection.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12900796 U0002-9297 (Print) Evaluation Studies Journal Article Research Support, Non-U.S. Gov't12900796SThe Centre for Genetic Anthropology, Department of Biology, London, United Kingdom.M~?Weber, J. L. Broman, K. W.2001BGenotyping for human whole-genome scans: past, present, and future77-96 Adv Genet42EForecasting Genetic Markers *Genome, Human *Genotype Humans PhenotypeyEfficient and effective whole-genome 10-cM short tandem repeat polymorphism (STRP) scans are now available. Doubling or tripling STRP density to an average spacing of 3-5 cM is readily achievable. However, if typing costs for diallelic polymorphisms can be brought close to, or preferably less than, one-third those of STRPs, then diallelics may gradually supplement or supplant STRPs in whole-genome scans. The power of higher density genome scans for gene map ping by association and for many other research and clinical applications is great. It would be wise to continue investing heavily for many years in genotyping technology.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11037315 (0065-2660 (Print) Journal Article Review11037315ZCenter for Medical Genetics, Marshfield Medical Research Foundation, Wisconsin 54449, USA.~?Weber, J. L. May, P. E.1989`Abundant class of human DNA polymorphisms which can be typed using the polymerase chain reaction388-96Am J Hum Genet443Alleles DNA/*genetics DNA-Directed DNA Polymerase Gene Frequency *Genetic Markers Humans Molecular Sequence Data *Polymorphism, Genetic *Repetitive Sequences, Nucleic AcidMareInterspersed DNA elements of the form (dC-dA)n.(dG-dT)n constitute one of the most abundant human repetitive DNA families. We report that specific human (dC-dA)n.(dG-dT)n blocks are polymorphic in length among individuals and therefore represent a vast new pool of potential genetic markers. Comparison of sequences from the literature for (dC-dA)n.(dG-dT)n blocks cloned two or more times revealed length polymorphisms in seven of eight cases. Variations in the lengths of 10 (dC-dA)n.(dG-dT)n blocks were directly demonstrated by amplifying the DNA within and immediately flanking the repeat blocks by using the polymerase chain reaction and then resolving the amplified DNA on polyacrylamide DNA sequencing gels. Use of the polymerase chain reaction to detect DNA polymorphisms offers improved sensitivity and speed compared with standard blotting and hybridization.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2916582 B0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't29165821Marshfield Medical Research Foundation, WI 54449. ~?Weber, J. L. Wong, C.1993&Mutation of human short tandem repeats1123-8 Hum Mol Genet28Base Sequence Cell Line, Transformed *Chromosomes, Human, Pair 19 Female Genetic Markers Genotype Humans Lymphocytes Male *Mutation Nuclear Family *Polymorphism, Genetic *Repetitive Sequences, Nucleic AcidAugA total of 20,000 parent-offspring transfers of alleles were examined through the genotyping within 40 CEPH reference families of 28 short tandem repeat polymorphisms (STRPs) located on chromosome 19. Forty-seven initial mutation events were detected in the STRPs using DNA from transformed lymphoblastoid cell lines, but less than half (39%) could be verified using DNA from untransformed cells. None of the cases where three alleles were observed in a single individual could be verified using DNA from untransformed cells. The average mutation rate for the chromosome 19 STRPs after correction for events which would not be detectable as Mendelian errors was 1.2 x 10(-3) per locus per gamete per generation. This rate may have been inflated by somatic as opposed to germline events. Observed mutation rates for individual STRPs ranged from 0 to 8 x 10(-3). The average mutation rate for tetranucleotide STRPs was nearly four times higher than the average rate for dinucleotide STRPs. For determination of the mode of mutation, events involving STRPs on other chromosomes were also examined. Of the events which were verified using DNA from untransformed lymphocytes or which were likely among those for which DNA from untransformed cells was not available: none were located at the sites of meiotic recombination, 91% involved the gain or loss of a single repeat unit, and 15 occurred in the male germline compared to 4 in the female germline (p = 0.01).ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8401493 F0964-6906 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8401493NCenter for Medical Genetics, Marshfield Medical Research Foundation, WI 54449.~?Weeks, D. E. Lange, K.19887The affected-pedigree-member method of linkage analysis315-26Am J Hum Genet422[Female Genetic Markers Humans *Linkage (Genetics) Male *Models, Genetic Pedigree StatisticsFebThis paper describes a generalization of the affected-sib-pair method of linkage analysis to pedigrees. By substituting identity-by-state relations for identity-by-descent relations, we develop a test statistic for detecting departures from independent segregation of disease and marker phenotypes. The statistic is based on the marker phenotypes of affected pedigree members only. Since it is more striking for distantly affected relatives to share a rare marker allele than a common marker allele, the statistic also includes a weighting factor based on allele frequency. The distributional properties of the statistic are investigated theoretically and by simulation. Part of the theoretical treatment entails generalizing Karigl's multiple-person kinship coefficients. When the test statistic is applied to pedigree data on Huntington disease, the null hypothesis of independent segregation between the marker locus and the disease locus is firmly rejected. In this case, as expected, there is a loss of power when compared with standard lod-score analysis. However, our statistic possesses the advantage of requiring no explicit assumptions about the mode of inheritance of the disease. This point is illustrated by application of the test statistic to data on rheumatoid arthritis.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3422543 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.3422543ADepartment of Biomathematics, UCLA School of Medicine 90024-1766.~?1Weeks, D. E. Sobel, E. O'Connell, J. R. Lange, K.1995AComputer programs for multilocus haplotyping of general pedigrees1506-7Am J Hum Genet566+*Algorithms *Haplotypes *Pedigree *SoftwareJunehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7762577 ^0002-9297 (Print) Letter Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.7762577? Weinberg, W.1908/Über den Nachweis der Vererbung beim Menschen.369-382Jh. Ver. Vaterlnat. Württemb.64W? Weir, B.S1996Genetic Data Analysis IISunderland, MA.Sinauer~?Weiss, K. M. Clark, A. G.2002>Linkage disequilibrium and the mapping of complex human traits19-24 Trends Genet181_Chromosome Mapping Genetics, Population Genome, Human Haplotypes Humans *Linkage DisequilibriumJanThe potential value of haplotypes defined by several single nucleotide polymorphisms has attracted recent interest. With sufficient linkage disequilibrium (LD), haplotypes could be used in association studies to map common alleles that might influence the susceptibility to common diseases, as well as for reconstructing the evolution of the genome. It has been proposed that a globally useful resource need only be based on high frequency variants, identified from a few modest samples. Rapid progress has been made in quantifying the pattern of human LD and haplotypes defined by such common variants within and among populations. However, the quality and utility of the proposed LD-based resource could be seriously compromised if important sampling and analytical factors are overlooked in its design. The LD map should be based on adequately justified criteria defined by sound population genetic principles.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11750696 F0168-9525 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.11750696xDepartment of Anthropology, Penn State University, 409 Carpenter Bldg, University Park, PA 16802-3404, USA. kmw4@psu.eduf~?Weiss, K. M. Terwilliger, J. D.20007How many diseases does it take to map a gene with SNPs?151-7 Nat Genet262zGenetic Diseases, Inborn/*genetics *Genetics, Medical Humans Linkage Disequilibrium Models, Genetic *Polymorphism, GeneticOctfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11017069 n1061-4036 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Review11017069pDepartments of Anthropology and Biology, Penn State University, University Park, Pennsylvania, USA. kmw4@psu.edug~?'Weiss, L. A. Pan, L. Abney, M. Ober, C.2006FThe sex-specific genetic architecture of quantitative traits in humans218-22 Nat Genet382Genetic Predisposition to Disease/*genetics *Genetics, Medical Genome, Human/genetics Humans Linkage (Genetics) Population Groups/genetics *Quantitative Trait, Heritable *Sex CharacteristicsFebMapping genetically complex traits remains one of the greatest challenges in human genetics today. In particular, gene-environment and gene-gene interactions, genetic heterogeneity and incomplete penetrance make thorough genetic dissection of complex traits difficult, if not impossible. Sex could be considered an environmental factor that can modify both penetrance and expressivity of a wide variety of traits. Sex is easily determined and has measurable effects on recognizable morphology; neurobiological circuits; susceptibility to autoimmune disease, diabetes, asthma, cardiovascular and psychiatric disease; and quantitative traits like blood pressure, obesity and lipid levels, among others. In this study, we evaluated sex-specific heritability and genome-wide linkages for 17 quantitative traits in the Hutterites. The results of this study could have important implications for mapping complex trait genes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16429159 1061-4036 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.16429159Z[1] Department of Human Genetics, The University of Chicago, Chicago, Illinois 60637, USA.~?dWeissenbach, J. Gyapay, G. Dib, C. Vignal, A. Morissette, J. Millasseau, P. Vaysseix, G. Lathrop, M.19923A second-generation linkage map of the human genome794-801Nature3596398Chromosome Mapping *Chromosomes, Human DNA/genetics Gene Library Genetic Markers *Genome, Human Genotype Heterozygote Humans *Linkage (Genetics) Polymerase Chain Reaction Repetitive Sequences, Nucleic AcidOct 29"A linkage map of the human genome has been constructed based on the segregation analysis of 814 newly characterized polymorphic loci containing short tracts of (C-A)n repeats in a panel of DNAs from eight large families. Statistical linkage analysis placed 813 of the markers into 23 linkage groups corresponding to the 22 autosomes and the X chromosome; 605 show a heterozygosity above 0.7 and 553 could be ordered with odds ratios above 1,000:1. The distance spanned corresponds to approximately 90% of the estimated length of the human genome.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1436057 B0028-0836 (Print) Journal Article Research Support, Non-U.S. Gov't1436057Genethon, Evry, France.]~?Whittemore, A. S.1996(Genome scanning for linkage: an overview704-16Am J Hum Genet593Female Genome, Human Humans Likelihood Functions *Linkage (Genetics) Male *Models, Genetic Multivariate Analysis Normal Distribution Pedigree Statistics, NonparametricSepSeveral different methods for linkage analysis are shown to arise from a single likelihood function L for the observed allele-sharing data at multiple markers in a chromosomal region. These include classical parametric lod score methods, nonparametric or "model-free" affected pedigree-member (APM) methods, and the Gaussian process method. Setting the methods in the context of the likelihood function L clarifies their underlying assumptions. A test statistic derived from L, the efficient score statistic, is introduced. It is asymptotically equivalent to the lod score, but it can be easier to compute when the penetrances and frequencies of alleles of the trait gene are not known. APM test statistics and the Gaussian lod score are shown to be special cases of efficient score statistics. This unified framework facilitates exploration of a range of models for the effects of a putative trait-predisposing gene, and it facilitates sensitivity analyses to examine the consequences of model misspecification.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8751872 X0002-9297 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.8751872rDepartment of Health Research and Policy, Stanford University School of Medicine, CA, USA. asw@osiris.stanford.edu~?Whittemore, A. S. Halpern, J.1994<A class of tests for linkage using affected pedigree members118-27 Biometrics501Alleles Biometry/*methods Female Genes, Dominant Genes, Recessive Genetic Markers *Genetic Techniques Humans *Linkage (Genetics) Lod Score Male Models, Genetic Models, Statistical *Pedigree Recombination, GeneticMar>We describe a class of nonparametric tests for linkage between a marker and a gene assumed to exist and to govern susceptibility to a disease. The tests are formed by assigning a score to each possible pattern of marker allele sharing (identity-by-descent) among affected pedigree members, and then averaging the scores over all patterns compatible with the observed marker genotype and genealogical relationship of the affected members. Different score functions give different tests. One function, which examines marker allele similarity across pairs of affected pedigree members, gives a test similar to that of Fimmers et al. (1989, in Multipoint Mapping and Linkage Based on Affected Pedigree Members: Genetic Analysis Workshop, R. C. Elston, M. A. Spence, S. E. Hodge, and J. W. MacCluer (eds), 123-128; City: Alan R. Liss). A second function examines allele similarity across arbitrary subsets, not just pairs, of affected members. The resulting test can be more powerful than the one based solely on pairs of affected members. The approach has several advantages: it does not require knowledge of the mode of disease inheritance; it does not require unambiguous determination of identity-by-descent at the marker; it does not suffer from variability due to chance allele similarity among affected members who are unrelated, such as spouses; it allows marker genotypes of unaffected members to contribute information on allele sharing among the affected; it permits calculation of exact P-values. Computational requirements limit the tests to many pedigrees with few (< 16) affected members.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8086596 F0006-341X (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8086596cDepartment of Health Research and Policy, Stanford University School of Medicine, California 94305.p~?Whittemore, A. S. Halpern, J.2006?Nonparametric linkage analysis using person-specific covariates369-79Genet Epidemiol305Chromosome Mapping/*methods Chromosomes, Human, Pair 19/*genetics Computer Simulation Databases, Genetic Humans Lod Score Male Pedigree *Polymorphism, Genetic Prostatic Neoplasms/*genetics *SoftwareJulLinkage analysis provides an important tool for mapping genes for complex disease. However its usefulness has been limited by inadequate marker density, inadequate sample sizes and the possibility that different genes account for different subtypes of the disease (phenotypic heterogeneity). The first two limitations can be addressed by high-density single nucleotide polymorphism (SNP) genotyping and the pooling of large sets of multiple-case families. Phenotypic heterogeneity can be addressed by analyses that weigh the contributions of affected family members according to characteristics of their disease phenotypes. Here we introduce a method for including such person-specific weights in nonparametric linkage analysis. We show with simulations that such weighting can provide stronger linkage signals when a causal polymorphism affects some manifestations of the disease more than others. We applied the method to prostate cancer linkage data in a region on chromosome 19p, and obtained higher lod scores by assigning weights of one to men with early-onset aggressive cancers, weights of zero to those with late-onset nonaggressive cancers, and intermediate weights to all other affected men. We have developed a modified version of GENEHUNTER that allows inclusion of person-specific weights in the nonparametric analyses. This program is freely available at http://med.stanford.edu/epidemiology/statisticalSoftware/weightedKAC.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16671107 F0741-0395 (Print) Journal Article Research Support, N.I.H., Extramural16671107Division of Epidemiology, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California 94305-5405, USA. alicesw@standford.edu~?&Wiener, H. Elston, R. C. Tiwari, H. K.2003\X-linked extension of the revised Haseman-Elston algorithm for linkage analysis in sib pairs97-107 Hum Hered552-3*Algorithms Chromosome Mapping *Chromosomes, Human, X Computer Simulation *Data Interpretation, Statistical Humans *Linkage (Genetics) Models, Genetic Siblings4Haseman and Elston (H-E) proposed a regression-based robust test of linkage between a marker and an autosomal quantitative trait locus, using the squared sib pair trait difference as a dependent variable and the proportion of alleles shared identical by descent by the sib pair as an independent variable. Several authors have proposed improvement of the original H-E's seminal work by using an optimal linear combination of squared sum and squared difference as the dependent variable. In this paper, we extend Haseman and Elston's sib pair method to an X-linked locus. We give a general formulation of the complete regression model and details of the regression coefficients in terms of variance components. Simulation results are presented to describe the power of this technique for a theoretical best case scenario.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12931048 !0001-5652 (Print) Journal Article12931048rDepartment of Epidemiology, University of Alabama at Birmingham, Birmingham, Ala. 35294-0022, USA. HWiener@uab.edu~? Wigginton, J. E. Abecasis, G. R.2005WPEDSTATS: descriptive statistics, graphics and quality assessment for gene mapping data3445-7Bioinformatics2116*Algorithms Chromosome Mapping/*methods Computer Graphics Data Interpretation, Statistical *Models, Genetic Models, Statistical Quality Control Sequence Analysis, DNA/*methods *Software *User-Computer InterfaceAug 15We describe a tool that produces summary statistics and basic quality assessments for gene-mapping data, accommodating either pedigree or case-control datasets. Our tool can also produce graphic output in the PDF format.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15947021 k1367-4803 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S.15947021Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48103, USA. wiggie@umich.edu Q~? Wigginton, J. E. Abecasis, G. R.2006aAn evaluation of the replicate pool method: quick estimation of genome-wide linkage peak p-values320-32Genet Epidemiol304Analysis of Variance *Genome *Linkage (Genetics) *Models, Genetic *Monte Carlo Method Proportional Hazards Models Statistics, NonparametricMayThe calculation of empirical p-values for genome-wide non-parametric linkage tests continues to present significant computational challenges for many complex disease mapping studies. The gold standard approach is to use gene dropping to simulate null genome scans. Unfortunately, this approach is too computationally expensive for many data sets of interest. An alternative, more efficient method for sampling null genome scans is to pre-calculate pools of family-specific statistics and then resample from these replicate pools to generate "pseudo-replicate" genome scans. In this study, we use simulations to explore properties of the replicate pool p-value estimator pRP and show that it provides an excellent approximation to the traditional gene-dropping estimator for significantly less computational effort. While the computational efficiency of the replicate pool estimator is noticeable in almost all data sets, by applying the replicate pool method to several previously characterized data sets we show that savings in computational effort can be especially significant (on the order of 10,000-fold or more) when one or more large families are analyzed. We also estimate replicate pool p-values for the schizophrenia data described by Abecasis et al. and show that pRP closely approximates gene-drop p-values for all linkage peaks reported for this study. Lastly, we expand upon Song et al.'s previous work by deriving a conservative estimator of the variance for PRP that can easily be computed in practical settings. We have implemented the replicate pool method along with our variance estimator in a new program called Pseudo, which is the first widely available automated implementation of the replicate pool method.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16832873 F0741-0395 (Print) Journal Article Research Support, N.I.H., Extramural16832873zCenter for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA. wiggie@umich.edu~?.Wigginton, J. E. Cutler, D. J. Abecasis, G. R.20053A note on exact tests of Hardy-Weinberg equilibrium887-93Am J Hum Genet765B*Genetics, Population Genotype Humans *Models, Genetic *StatisticsMayDeviations from Hardy-Weinberg equilibrium (HWE) can indicate inbreeding, population stratification, and even problems in genotyping. In samples of affected individuals, these deviations can also provide evidence for association. Tests of HWE are commonly performed using a simple chi2 goodness-of-fit test. We show that this chi2 test can have inflated type I error rates, even in relatively large samples (e.g., samples of 1,000 individuals that include approximately 100 copies of the minor allele). On the basis of previous work, we describe exact tests of HWE together with efficient computational methods for their implementation. Our methods adequately control type I error in large and small samples and are computationally efficient. They have been implemented in freely available code that will be useful for quality assessment of genotype data and for the detection of genetic association or population stratification in very large data sets.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15789306 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.15789306oCenter for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.f~?Wijsman, E. M.19875A deductive method of haplotype analysis in pedigrees356-73Am J Hum Genet413gGenetic Markers *Genotype Humans *Models, Genetic *Pedigree Rh-Hr Blood-Group System/genetics *SoftwareSepDerivation of haplotypes from pedigree data by means of likelihood techniques requires large computational resources and is thus highly limited in terms of the complexity of problems that can be analyzed. The present paper presents 20 rules of logic that are both necessary and sufficient for deriving haplotypes by means of nonstatistical techniques. As a result, automated haplotype analysis that uses these rules is fast and efficient, requiring computer memory that increases only linearly (rather than exponentially) with family size and the number of factors under analysis. Some error analysis is also possible. The rules are completely general with regard to any system of completely linked, discrete genetic markers that are autosomally inherited. There are no limitations on pedigree structure or the amount of missing data, although the existence of incomplete data usually reduces the fraction of haplotypes that can be completely determined.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3115093 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.3115093~?Williams, J. T. Blangero, J.1999{Comparison of variance components and sibpair-based approaches to quantitative trait linkage analysis in unselected samples113-34Genet Epidemiol162Chromosome Mapping False Positive Reactions Humans Likelihood Functions *Linkage (Genetics) Lod Score Mathematical Computing *Models, Statistical *Quantitative Trait, HeritableWe compared the statistical performance of sibpair-based and variance components approaches to multipoint linkage analysis of a quantitative trait in unselected samples. As a benchmark dataset, we used the simulated family data from Genetic Analysis Workshop 10 [Goldin et al., 1997], and each method was used to screen all 200 replications of the GAW10 genome for evidence of linkage to quantitative trait Q1. The sibpair and variance components methods were each applied to datasets comprising single-sibpairs and complete sibships, and for further comparison we also applied the variance components method to the nuclear family and extended pedigree datasets. For each analysis, the unbiasedness and efficiency of parameter estimation, the power to detect linkage, and the Type I error rate were estimated empirically. Sibpair and variance components methods exhibited comparable performance in terms of the unbiasedness of the estimate of QTL location and the Type I error rate. Within the single-sibpair and sibship sampling units, the variance components approach gave consistently superior power and efficiency of parameter estimation. Within each method, the statistical performance was improved by the use of the larger and more informative sampling units.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10030396 X0741-0395 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.10030396Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas 78245-0549, USA. jeffw@darwin.sfbr.org~?Williams, J. T. Blangero, J.1999NPower of variance component linkage analysis to detect quantitative trait loci545-63 Ann Hum Genet63Pt 6Analysis of Variance Biometry Computer Simulation Female Humans Likelihood Functions *Linkage (Genetics) Male Models, Genetic Nuclear Family Parents Pedigree *Quantitative Trait, Heritable Sample SizeNovExpressions are derived for the sample size required to achieve a given power in variance component linkage analysis of a quantitative trait in unascertained samples. For simplicity an additive model, comprising effects due to a single QTL, residual additive genetic factors, and individual-specific random environmental variation, is considered. Equations are given relating sample size to trait heritability for sibpairs, sib trios, nuclear families having two and three sibs, and arbitrary relative pairs. The effects of nonzero residual additive genetic variance and parental information are discussed, and a scale relationship for sample sizes with sibships and nuclear families is derived. For larger sampling structures such as extended pedigrees the inheritance space is randomly sampled and the relevant equations are solved numerically. Comparative power curves are presented for sibships of size 2-4 and for an extended pedigree of 48 individuals. Simulation results for sibpairs confirm the validity of the theoretical results.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11246457 30003-4800 (Print) Comparative Study Journal Article11246457iDepartment of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas 78245-0549, USA.~?*Williams, J. T. Duggirala, R. Blangero, J.1997Statistical properties of a variance components method for quantitative trait linkage analysis in nuclear families and extended pedigrees1065-70Genet Epidemiol146Analysis of Variance Bias (Epidemiology) Chromosome Mapping *Chromosomes, Human, Pair 5 Humans *Linkage (Genetics) Lod Score *Nuclear Family Pedigree Predictive Value of Tests *Quantitative Trait, Heritableehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9433625 g0741-0395 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.9433625]Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA. $~?9Williams, J. T. Van Eerdewegh, P. Almasy, L. Blangero, J.1999Joint multipoint linkage analysis of multivariate qualitative and quantitative traits. I. Likelihood formulation and simulation results1134-47Am J Hum Genet654ZChromosome Mapping/*methods/*statistics & numerical data Chromosomes, Human, Pair 8/genetics Chromosomes, Human, Pair 9/genetics *Computer Simulation Environment Female Humans Likelihood Functions Linkage (Genetics)/*genetics Lod Score Male Models, Genetic Multivariate Analysis Pedigree *Quantitative Trait, Heritable Research Design Sample SizeOctWe describe a variance-components method for multipoint linkage analysis that allows joint consideration of a discrete trait and a correlated continuous biological marker (e.g., a disease precursor or associated risk factor) in pedigrees of arbitrary size and complexity. The continuous trait is assumed to be multivariate normally distributed within pedigrees, and the discrete trait is modeled by a threshold process acting on an underlying multivariate normal liability distribution. The liability is allowed to be correlated with the quantitative trait, and the liability and quantitative phenotype may each include covariate effects. Bivariate discrete-continuous observations will be common, but the method easily accommodates qualitative and quantitative phenotypes that are themselves multivariate. Formal likelihood-based tests are described for coincident linkage (i.e., linkage of the traits to distinct quantitative-trait loci [QTLs] that happen to be linked) and pleiotropy (i.e., the same QTL influences both discrete-trait status and the correlated continuous phenotype). The properties of the method are demonstrated by use of simulated data from Genetic Analysis Workshop 10. In a companion paper, the method is applied to data from the Collaborative Study on the Genetics of Alcoholism, in a bivariate linkage analysis of alcoholism diagnoses and P300 amplitude of event-related brain potentials.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10486333 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10486333|Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX 78245-0549, USA. jeffw@darwin.sfbr.org l~?+Wiltshire, S. Cardon, L. R. McCarthy, M. I.2002VEvaluating the results of genomewide linkage scans of complex traits by locus counting1175-82Am J Hum Genet715Computer Simulation Diabetes Mellitus, Type 2/genetics Genetic Predisposition to Disease Genome Humans *Linkage (Genetics) Lod Score Models, Genetic Multifactorial Inheritance/*geneticsNovThe evaluation of results from primary genomewide linkage scans of complex human traits remains an area of importance and considerable debate. Apart from the usual assessment of statistical significance by use of asymptotic and empirical calculations, an additional means of evaluation--based on counting the number of distinct regions showing evidence of linkage--is possible. We have explored the characteristics of such a locus-counting method over a range of experimental conditions typically encountered during genomewide scans for complex trait loci. Under the null hypothesis, factors that have an impact on the informativeness of the data--such as map density, availability of parental data, and completeness of genotyping--are seen to markedly influence the number of regions of excess allele sharing and the empirically derived genomewide significance of the associated LOD score thresholds. In some circumstances, the expected number of regions is less than one-quarter of that predicted under the assumption of a dense map and complete extraction of inheritance information. We have applied this method to a previously analyzed data set--the Warren 2 genome scan for type 2-diabetes susceptibility--and demonstrate that more regions showing evidence for linkage were observed in the primary genome scan than would be expected by chance, across the whole range of LOD scores, even though no single linkage result achieved empirical genomewide statistical significance. Locus counting may be useful in assessing the results from genome scans for complex traits in general, especially because relatively few scans generate evidence for linkage reaching genomewide significance by dense-map criteria. By taking account of the effects of reduced data informativeness on the expected number of regions showing evidence for linkage, a more meaningful, and less conservative, evaluation of the results from such linkage studies is possible.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12355401 U0002-9297 (Print) Evaluation Studies Journal Article Research Support, Non-U.S. Gov't12355401Imperial College Genetics and Genomics Research Institute, Imperial College, London, United Kingdom. steven.wiltshire@well.ox.ac.ukm? Winston, W.19910Operations Research: Applications and Algorithms Boston, MAPWS-Kent~?(Witte, J. S. Elston, R. C. Cardon, L. R.2000=On the relative sample size required for multiple comparisons369-72Stat Med1931*Models, Statistical Research Design *Sample SizeFeb 15Multiple comparisons are commonly made in epidemiologic and genetic research. How to appropriately adjust for multiple comparisons remains a controversial issue. This note demonstrates, however, that large increases in the number of comparisons has a limited effect on the sample size required to maintain an experimentwise alpha-level. In particular, the relative sample size required increases only linearly with the logarithm of the number of comparisons made.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10649302 X0277-6715 (Print) Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.10649302Department of Epidemiology and Biostatistics, Case Western Reserve University, 2500 MetroHealth Drive, Cleveland OH 44109-1998, USA. witte@darwin.cwru.edu '~?+Witte, J. S. Gauderman, W. J. Thomas, D. C.1999Asymptotic bias and efficiency in case-control studies of candidate genes and gene-environment interactions: basic family designs693-705Am J Epidemiol1498Bias (Epidemiology) Case-Control Studies Environmental Exposure/adverse effects/*statistics & numerical data Female Genetic Predisposition to Disease/*genetics Humans Linkage Disequilibrium Male Models, Genetic RiskApr 15+Case-control designs that use population controls are compared with those that use controls selected from their relatives (i.e., siblings, cousins, or "pseudosibs" based on parental alleles) for estimating the effect of candidate genes and gene-environment interactions. The authors first evaluate the asymptotic bias in relative risk estimates resulting from using population controls when there is confounding due to population stratification. Using siblings or pseudosibs as controls completely addresses this issue, whereas cousins provide only partial protection from population stratification. Next, they show that the conventional conditional likelihood for matched case-control studies can give asymptotically biased effect estimates when applied to the pseudosib approach; the asymptotic bias is toward the null and disappears with disease rarity. They show how to reparameterize the pseudosib likelihood so this approach gives consistent effect estimates. They then show that the designs using population or pseudosib controls are generally the most efficient for estimating the main effect of a candidate gene, followed in efficiency by the design using cousins. Finally, they show that the design using sibling controls can be quite efficient when studying gene-environment interactions. In addition to asymptotic bias and efficiency issues, family-based designs might benefit from a higher motivation to participate among cases' relatives, but these designs have the disadvantage that many potential cases will be excluded from study by having no available controls.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10206618 F0002-9262 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.10206618mDepartment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44109-1998, USA. C~?0Wittke-Thompson, J. K. Pluzhnikov, A. Cox, N. J.2005DRational inferences about departures from Hardy-Weinberg equilibrium967-86Am J Hum Genet766*Alleles Bias (Epidemiology) Case-Control Studies Chromosome Mapping *Gene Frequency Genetic Markers *Genetic Predisposition to Disease Genetics, Population Genotype Homozygote Humans *Models, Genetic *Models, Statistical SoftwareJunmPrevious studies have explored the use of departure from Hardy-Weinberg equilibrium (DHW) for fine mapping Mendelian disorders and for general fine mapping. Other studies have used Hardy-Weinberg tests for genotyping quality control. To enable investigators to make rational decisions about whether DHW is due to genotyping error or to underlying biology, we developed an analytic framework and software to determine the parameter values for which DHW might be expected for common diseases. We show analytically that, for a general disease model, the difference between population and Hardy-Weinberg expected genotypic frequencies (delta) at the susceptibility locus is a function of the susceptibility-allele frequency (q), heterozygote relative risk (beta), and homozygote relative risk (gamma). For unaffected control samples, is a function of risk in nonsusceptible homozygotes (alpha), the population prevalence of disease (KP), q, beta, and gamma. We used these analytic functions to calculate and the number of cases or controls needed to detect DHW for a range of genetic models consistent with common diseases (1.1 < or = gamma < or = 10 and 0.005 < or = KP < or = 0.2). Results suggest that significant DHW can be expected in relatively small samples of patients over a range of genetic models. We also propose a goodness-of-fit test to aid investigators in determining whether a DHW observed in the context of a case-control study is consistent with a genetic disease model. We illustrate how the analytic framework and software can be used to help investigators interpret DHW in the context of association studies of common diseases.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15834813 k0002-9297 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S.15834813PDepartment of Human Genetics, The University of Chicago, Chicago, IL 60637, USA.v?Wonnacott, R.J Wonnacott, T.H1985!Introductory Statistics, 4th Edn.TorontoJohn Wiley & Sons|~? Wray, N. R.2005{Allele frequencies and the r2 measure of linkage disequilibrium: impact on design and interpretation of association studies87-94Twin Res Hum Genet82Algorithms Chromosome Mapping Data Interpretation, Statistical Gene Frequency/*genetics Genetic Markers/genetics Genotype Humans Linkage Disequilibrium/*genetics Polymorphism, Single Nucleotide/genetics Research Design/statistics & numerical data Variation (Genetics)/geneticsAprThe design and interpretation of genetic association studies depends on the relationship between the genotyped variants and the underlying functional variant, often parameterized as the squared correlation or r(2) measure of linkage disequilibrium between two loci. While it has long been recognized that placing a constraint on ther(2) between two loci also places a constraint on the difference in frequencies between the coupled alleles, this constraint has not been quantified. Here, quantification of this severe constraint is presented. For example, for r(2) >/= .8, the maximum difference in allele frequency is +/- .06 which occurs when one locus has allele frequency .5. For r(2) >/= .8 and allele frequency at one locus of .1, the maximum difference in allele frequency at the second locus is only +/- .02. The impact on the design and interpretation of association studies is discussed.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15901470 B1832-4274 (Print) Journal Article Research Support, Non-U.S. Gov't15901470\Department of Medical Sciences, University of Edinburgh, United Kingdom. naomi.wray@ed.ac.uk~? Wright, F. A.1997CThe phenotypic difference discards sib-pair QTL linkage information740-2Am J Hum Genet603PChromosome Mapping Humans *Linkage (Genetics) Models, Genetic Phenotype SoftwareMarehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9042938 E0002-9297 (Print) Comment Letter Research Support, U.S. Gov't, P.H.S.9042938 V~?Wright, M. De Geus, E. Ando, J. Luciano, M. Posthuma, D. Ono, Y. Hansell, N. Van Baal, C. Hiraishi, K. Hasegawa, T. Smith, G. Geffen, G. Geffen, L. Kanba, S. Miyake, A. Martin, N. Boomsma, D.2001<Genetics of cognition: outline of a collaborative twin study48-56Twin Res41Adolescent Adult Aged Aptitude Tests Clinical Protocols Cognition/*physiology *Cooperative Behavior Electrophysiology Female Humans Intelligence/genetics Male Memory/physiology Middle Aged Phenotype Psychometrics Reaction Time/genetics Task Performance and Analysis Twin StudiesFebA multidisciplinary collaborative study examining cognition in a large sample of twins is outlined. A common experimental protocol and design is used in The Netherlands, Australia and Japan to measure cognitive ability using traditional IQ measures (i.e., psychometric IQ), processing speed (e.g., reaction time [RT] and inspection time [IT]), and working memory (e.g., spatial span, delayed response [DR] performance). The main aim is to investigate the genetic covariation among these cognitive phenotypes in order to use the correlated biological markers in future linkage and association analyses to detect quantitative-trait loci (QTLs). We outline the study and methodology, and report results from our preliminary analyses that examines the heritability of processing speed and working memory indices, and their phenotypic correlation with IQ. Heritability of Full Scale IQ was 87% in the Netherlands, 83% in Australia, and 71% in Japan. Heritability estimates for processing speed and working memory indices ranged from 33-64%. Associations of IQ with RT and IT (-0.28 to -0.36) replicated previous findings with those of higher cognitive ability showing faster speed of processing. Similarly, significant correlations were indicated between IQ and the spatial span working memory task (storage [0.31], executive processing [0.37]) and the DR working memory task (0.25), with those of higher cognitive ability showing better memory performance. These analyses establish the heritability of the processing speed and working memory measures to be used in our collaborative twin study of cognition, and support the findings that individual differences in processing speed and working memory may underlie individual differences in psychometric IQ.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11665325 B1369-0523 (Print) Journal Article Research Support, Non-U.S. Gov't11665325sGenetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia. margieW@qimr.edu.au?Wright, M.J. Martin, N.2004NBrisbane Adolescent Twin Study: outline of study methods and research projects65-78Aust. J. Psychol.56`~? Wright, S.1921,Systems of Mating. V. General Considerations167-78Genetics62Marfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17245962 !0016-6731 (Print) Journal Article17245962UBureau of Animal Industry, United States Department of Agriculture, Washington, D. C.b~? Wright, S.1921/Systems of Mating. IV. the Effects of Selection162-6Genetics62Marfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17245961 !0016-6731 (Print) Journal Article17245961UBureau of Animal Industry, United States Department of Agriculture, Washington, D. C.{~? Wright, S.1921GSystems of Mating. III. Assortative Mating Based on Somatic Resemblance144-61Genetics62Marfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17245960 !0016-6731 (Print) Journal Article17245960UBureau of Animal Industry, United States Department of Agriculture, Washington, D. C.~? Wright, S.1921[Systems of Mating. II. the Effects of Inbreeding on the Genetic Composition of a Population124-43Genetics62Marfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17245959 !0016-6731 (Print) Journal Article17245959UBureau of Animal Industry, United States Department of Agriculture, Washington, D. C.~~? Wright, S.1921JSystems of Mating. I. the Biometric Relations between Parent and Offspring111-23Genetics62Marfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17245958 !0016-6731 (Print) Journal Article17245958UBureau of Animal Industry, United States Department of Agriculture, Washington, D. C.~?Wu, C. C. Shete, S. Amos, C. I.2005LLinkage analysis of affected sib pairs allowing for parent-of-origin effects113-26 Ann Hum Genet69Pt 1Alleles Chromosome Mapping/*methods Female Genetic Markers/genetics *Genetic Predisposition to Disease Genomic Imprinting/*genetics Humans *Linkage (Genetics) Lod Score Male Models, Genetic *Parents *Quantitative Trait, Heritable SiblingsJanParent-of-origin effects, also known as genomic imprinting, exist for many mammalian genes. For imprinted genes the expression of an allele depends upon the sex of the transmitting parent. Here we have developed a method based on alleles that are shared identical by descent by affected sib pairs, that allows for parent-of-origin effects. Our method allows for sex-specific recombination rates, an important consideration in studying imprinted genes. We have also derived a tetrahedron for the true identical-by-descent frequencies accounting for parent-of-origin effects. Using this tetrahedron, we propose a robust generalized minmax test for linkage and discuss its properties in the presence of genomic imprinting. We have also performed power comparisons of various allele sharing tests and provide regions of the tetrahedron in which the different tests are optimal. We also provide useful strategies to determine the optimal tests to use while performing a genome scan.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15638832 F0003-4800 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.15638832Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 189, Houston, TX 77030, USA. ccwu@mdanderson.orgs~?Xu, G. F. O'Connell, P. Viskochil, D. Cawthon, R. Robertson, M. Culver, M. Dunn, D. Stevens, J. Gesteland, R. White, R. et al.,1990BThe neurofibromatosis type 1 gene encodes a protein related to GAP599-608Cell623Amino Acid Sequence Animals Base Sequence Cattle Chromosomes, Human, Pair 1 Chromosomes, Human, Pair 17 Cloning, Molecular GTPase-Activating Proteins Gene Library Genes Humans Information Systems Molecular Sequence Data Neurofibromatosis 1/*genetics Neurofibromin 1 Oligonucleotide Probes Polymerase Chain Reaction Proteins/*genetics Sequence Homology, Nucleic Acid Translocation, Genetic ras GTPase-Activating ProteinsAug 10cDNA walking and sequencing have extended the open reading frame for the neurofibromatosis type 1 gene (NF1). The new sequence now predicts 2485 amino acids of the NF1 peptide. A 360 residue region of the new peptide shows significant similarity to the known catalytic domains of both human and bovine GAP (GTPase activating protein). A much broader region, centered around this same 360 amino acid sequence, is strikingly similar to the yeast IRA1 product, which has a similar amino acid sequence and functional homology to mammalian GAP. This evidence suggests that NF1 encodes a cytoplasmic GAP-like protein that may be involved in the control of cell growth by interacting with proteins such as the RAS gene product. Mapping of the cDNA clones has confirmed that NF1 spans a t(1;17) translocation mutation and that three active genes lie within an intron of NF1, but in opposite orientation.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2116237 }0092-8674 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.2116237ZDepartment of Human Genetics, University of Utah School of Medicine, Salt Lake City 84132.~?"Xu, X. Weiss, S. Xu, X. Wei, L. J.2000LA unified Haseman-Elston method for testing linkage with quantitative traits1025-8Am J Hum Genet674Alleles Chromosome Mapping/*methods/*statistics & numerical data Gene Frequency/genetics Genotype Humans Linear Models Linkage (Genetics)/*genetics Matched-Pair Analysis Models, Genetic Nuclear Family *Quantitative Trait, HeritableOctmThe Haseman and Elston (H-E) method uses a simple linear regression to model the squared trait difference of sib pairs with the shared allele identical by descent (IBD) at marker locus for linkage testing. Under this setting, the squared mean-corrected trait sum is also linearly related to the IBD sharing. However, the resulting slope estimate for either model is not efficient. In this report, we propose a simple linkage test that optimally uses information from the estimates of both models. We also demonstrate that the new test is more powerful than both the traditional one and the recently revisited H-E methods.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10961909 !0002-9297 (Print) Journal Article10961909lProgram for Population Genetics, Harvard School of Public Health, Boston, MA, 02115, USA. xin_xu@harvard.edu ~?Zollner, S. Pritchard, J. K.2005KCoalescent-based association mapping and fine mapping of complex trait loci1071-92Genetics1692Alleles Bayes Theorem Calpain/genetics Chromosome Mapping/*methods/statistics & numerical data Computer Simulation Cystic Fibrosis/genetics Cystic Fibrosis Transmembrane Conductance Regulator/genetics Diabetes Mellitus, Type 2/genetics Disease Susceptibility Haplotypes Humans Likelihood Functions Linkage Disequilibrium Markov Chains Models, Genetic Monte Carlo Method Mutation Pedigree Penetrance Polymorphism, Single Nucleotide *Quantitative Trait, Heritable Recombination, Genetic Variation (Genetics)FebuWe outline a general coalescent framework for using genotype data in linkage disequilibrium-based mapping studies. Our approach unifies two main goals of gene mapping that have generally been treated separately in the past: detecting association (i.e., significance testing) and estimating the location of the causative variation. To tackle the problem, we separate the inference into two stages. First, we use Markov chain Monte Carlo to sample from the posterior distribution of coalescent genealogies of all the sampled chromosomes without regard to phenotype. Then, averaging across genealogies, we estimate the likelihood of the phenotype data under various models for mutation and penetrance at an unobserved disease locus. The essential signal that these models look for is that in the presence of disease susceptibility variants in a region, there is nonrandom clustering of the chromosomes on the tree according to phenotype. The extent of nonrandom clustering is captured by the likelihood and can be used to construct significance tests or Bayesian posterior distributions for location. A novelty of our framework is that it can naturally accommodate quantitative data. We describe applications of the method to simulated data and to data from a Mendelian locus (CFTR, responsible for cystic fibrosis) and from a proposed complex trait locus (calpain-10, implicated in type 2 diabetes).fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15489534 }0016-6731 (Print) Comparative Study Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S.15489534qDepartment of Human Genetics, University of Chicago, Chicago, Illinois 60637, USA. szoellne@genetics.uchicago.edu~?Zonderman, A. B.1986aTwins, families, and the psychology of individual differences: the legacy of Steven G. Vandenberg11-24 Behav Genet161_*Aptitude Genetics, Behavioral History, 20th Century Humans *Individuality *Twins United StatesJanehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3518695 >0001-8244 (Print) Biography Historical Article Journal Article3518695Z~?Zondervan, K. T. Cardon, L. R.2004FThe complex interplay among factors that influence allelic association89-100 Nat Rev Genet52Alleles Case-Control Studies Gene Frequency Genetic Markers *Genetic Predisposition to Disease Humans Linkage Disequilibrium *Models, GeneticFebfhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14735120 I1471-0056 (Print) Journal Article Research Support, Non-U.S. Gov't Review14735120dWellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.? Yamazaki, T. 1977?The effects of overdominance of linkage in a multilocus system.227-236 Genetics 86~?Yan, H. Papadopoulos, N. Marra, G. Perrera, C. Jiricny, J. Boland, C. R. Lynch, H. T. Chadwick, R. B. de la Chapelle, A. Berg, K. Eshleman, J. R. Yuan, W. Markowitz, S. Laken, S. J. Lengauer, C. Kinzler, K. W. Vogelstein, B.2000"Conversion of diploidy to haploidy723-4Nature4036771iAnimals Base Pair Mismatch Carrier Proteins Cell Fusion Cell Line Cohort Studies Colorectal Neoplasms, Hereditary Nonpolyposis/genetics DNA Mutational Analysis/methods DNA Repair/genetics *DNA-Binding Proteins *Diploidy *Genetic Techniques *Haploidy Humans Mice MutS Homolog 2 Protein Neoplasm Proteins/genetics Nuclear Proteins Proto-Oncogene Proteins/geneticsFeb 17fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10693791 !0028-0836 (Print) Journal Article10693791Howard Hughes Medical Institute, The Oncology Center, Department of Mathematical Sciences, The Johns Hopkins University, Baltimore, Maryland 21231, USA. ~?Young, J. I. Zoghbi, H. Y.2004uX-chromosome inactivation patterns are unbalanced and affect the phenotypic outcome in a mouse model of rett syndrome511-20Am J Hum Genet743!Animals *Chromosomal Proteins, Non-Histone DNA-Binding Proteins/genetics *Disease Models, Animal *Dosage Compensation, Genetic Fluorescent Antibody Technique Methyl-CpG-Binding Protein 2 Mice Microscopy, Confocal Mutation Phenotype *Repressor Proteins Rett Syndrome/*genetics *X ChromosomeMarRett syndrome (RTT), a neurodevelopmental disorder affecting mostly females, is caused by mutations in the X-linked gene encoding methyl-CpG-binding protein 2 (MeCP2). Although the majority of girls with classic RTT have a random pattern of X-chromosome inactivation (XCI), nonbalanced patterns have been observed in patients carrying mutant MECP2 and, in some cases, account for variability of phenotypic manifestations. We have generated an RTT mouse model that recapitulates all major aspects of the human disease, but we found that females exhibit a high degree of phenotypic variability beyond what is observed in human patients with similar mutations. To evaluate whether XCI influences the phenotypic outcome of Mecp2 mutation in the mouse, we studied the pattern of XCI at the single-cell level in brains of heterozygous females. We found that XCI patterns were unbalanced, favoring expression of the wild-type allele, in most mutant females. It is notable that none of the animals had nonrandom XCI favoring the mutant allele. To explore why the XCI patterns favored expression of the wild-type allele, we studied primary neuronal cultures from Mecp2-mutant mice and found selective survival of neurons in which the wild-type X chromosome was active. Quantitative analysis indicated that fewer phenotypes are observed when a large percentage of neurons have the mutant X chromosome inactivated. The study of neuronal XCI patterns in a large number of female mice carrying a mutant Mecp2 allele highlights the importance of MeCP2 for neuronal viability. These findings also raise the possibility that there are human females who carry mutant MECP2 alleles but are not recognized because their phenotypes are subdued owing to favorable XCI patterns.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14973779 g0002-9297 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.14973779_Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.~?Yu, A. Zhao, C. Fan, Y. Jang, W. Mungall, A. J. Deloukas, P. Olsen, A. Doggett, N. A. Ghebranious, N. Broman, K. W. Weber, J. L.2001<Comparison of human genetic and sequence-based physical maps951-3Nature4096822Female Humans Linkage Disequilibrium Male *Physical Chromosome Mapping *Recombination, Genetic Sex Characteristics Tandem Repeat SequencesFeb 15Recombination is the exchange of information between two homologous chromosomes during meiosis. The rate of recombination per nucleotide, which profoundly affects the evolution of chromosomal segments, is calculated by comparing genetic and physical maps. Human physical maps have been constructed using cytogenetics, overlapping DNA clones and radiation hybrids; but the ultimate and by far the most accurate physical map is the actual nucleotide sequence. The completion of the draft human genomic sequence provides us with the best opportunity yet to compare the genetic and physical maps. Here we describe our estimates of female, male and sex-average recombination rates for about 60% of the genome. Recombination rates varied greatly along each chromosome, from 0 to at least 9 centiMorgans per megabase (cM Mb(-1)). Among several sequence and marker parameters tested, only relative marker position along the metacentric chromosomes in males correlated strongly with recombination rate. We identified several chromosomal regions up to 6 Mb in length with particularly low (deserts) or high (jungles) recombination rates. Linkage disequilibrium was much more common and extended for greater distances in the deserts than in the jungles.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11237020 0028-0836 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.11237020ZCenter for Medical Genetics, Marshfield Medical Research Foundation, Wisconsin 54449, USA.~?#Yu, X. Knott, S. A. Visscher, P. M.2004Theoretical and empirical power of regression and maximum-likelihood methods to map quantitative trait loci in general pedigrees17-26Am J Hum Genet751*Chromosome Mapping Computer Simulation Female Humans *Likelihood Functions Male Models, Genetic Models, Statistical Nuclear Family Pedigree *Quantitative Trait Loci Regression Analysis Twin StudiesJul Both theoretical calculations and simulation studies have been used to compare and contrast the statistical power of methods for mapping quantitative trait loci (QTLs) in simple and complex pedigrees. A widely used approach in such studies is to derive or simulate the expected mean test statistic under the alternative hypothesis of a segregating QTL and to equate a larger mean test statistic with larger power. In the present study, we show that, even when the test statistic under the null hypothesis of no linkage follows a known asymptotic distribution (the standard being chi(2)), it cannot be assumed that the distribution under the alternative hypothesis is noncentral chi(2). Hence, mean test statistics cannot be used to indicate power differences, and a comparison between methods that are based on simulated average test statistics may lead to the wrong conclusion. We illustrate this important finding, through simulations and analytical derivations, for a recently proposed new regression method for the analysis of general pedigrees to map quantitative trait loci. We show that this regression method is not necessarily more powerful nor computationally more efficient than a maximum-likelihood variance-component approach. We advocate the use of empirical power to compare trait-mapping methods.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15152343 T0002-9297 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't15152343RSchool of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.~?RZaykin, D. V. Westfall, P. H. Young, S. S. Karnoub, M. A. Wagner, M. J. Ehm, M. G.2002Testing association of statistically inferred haplotypes with discrete and continuous traits in samples of unrelated individuals79-91 Hum Hered532iAlgorithms Computer Simulation Gene Frequency *Haplotypes Humans Models, Statistical *Regression AnalysisThere have been increasing efforts to relate drug efficacy and disease predisposition with genetic polymorphisms. We present statistical tests for association of haplotype frequencies with discrete and continuous traits in samples of unrelated individuals. Haplotype frequencies are estimated through the expectation-maximization algorithm, and each individual in the sample is expanded into all possible haplotype configurations with corresponding probabilities, conditional on their genotype. A regression-based approach is then used to relate inferred haplotype probabilities to the response. The relationship of this technique to commonly used approaches developed for case-control data is discussed. We confirm the proper size of the test under H(0) and find an increase in power under the alternative by comparing test results using inferred haplotypes with single-marker tests using simulated data. More importantly, analysis of real data comprised of a dense map of single nucleotide polymorphisms spaced along a 12-cM chromosomal region allows us to confirm the utility of the haplotype approach as well as the validity and usefulness of the proposed statistical technique. The method appears to be successful in relating data from multiple, correlated markers to response.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12037407 40001-5652 (Print) Evaluation Studies Journal Article12037407aGlaxoSmithKline Inc., Discovery Genetics, Research Triangle Park, NC 27709, USA. dvz90620@gsk.com~?Zhang, H. Risch, N.1996zMapping quantitative-trait loci in humans by use of extreme concordant sib pairs: selected sampling by parental phenotypes951-7Am J Hum Genet594qChromosome Mapping/*methods Female Genotype Humans *Linkage (Genetics) Male Models, Genetic Phenotype ProbabilityOct&In two previous articles, we have considered sample sizes required to detect linkage for mapping quantitative-trait loci in humans, using extreme discordant sib pairs. Here, we examine further the use of extreme concordant sib pairs but consider the effect of parents' phenotypes. Sample sizes necessary to obtain a power of 80% with concordant sib pairs at a significance level of .0001 are given, stratified by parental phenotypes. When there is no residual correlation between sibs, the parental phenotypes have little impact on the sample sizes. When residual correlations between sibs exist, we show, however, that power can be considerably reduced by including extreme sib pairs when the parents also have similarly extreme values. Thus, we recommend the exclusion of such pairs from linkage studies. This recommendation reduces the required sample sizes by 3- to 28-fold. The degree of saving in the required sample sizes varies among different models and allele frequencies. The reduction is most dramatic (a 28-fold reduction) for a rare recessive gene.ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8808613 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.8808613Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA. heping.zhang@yale.edu H~?,Zhang, K. Calabrese, P. Nordborg, M. Sun, F.2002^Haplotype block structure and its applications to association studies: power and study designs1386-94Am J Hum Genet716Algorithms Case-Control Studies Chromosome Mapping/*methods/statistics & numerical data Computer Simulation Genetic Markers/genetics Haplotypes/*genetics Humans Linkage Disequilibrium/genetics Polymorphism, Single Nucleotide/*genetics Recombination, Genetic/genetics Research DesignDecRecent studies have shown that the human genome has a haplotype block structure, such that it can be divided into discrete blocks of limited haplotype diversity. In each block, a small fraction of single-nucleotide polymorphisms (SNPs), referred to as "tag SNPs," can be used to distinguish a large fraction of the haplotypes. These tag SNPs can potentially be extremely useful for association studies, in that it may not be necessary to genotype all SNPs; however, this depends on how much power is lost. Here we develop a simulation study to quantitatively assess the power loss for a variety of study designs, including case-control designs and case-parental control designs. First, a number of data sets containing case-parental or case-control samples are generated on the basis of a disease model. Second, a small fraction of case and control individuals in each data set are genotyped at all the loci, and a dynamic programming algorithm is used to determine the haplotype blocks and the tag SNPs based on the genotypes of the sampled individuals. Third, the statistical power of tests was evaluated on the basis of three kinds of data: (1) all of the SNPs and the corresponding haplotypes, (2) the tag SNPs and the corresponding haplotypes, and (3) the same number of randomly chosen SNPs as the number of tag SNPs and the corresponding haplotypes. We study the power of different association tests with a variety of disease models and block-partitioning criteria. Our study indicates that the genotyping efforts can be significantly reduced by the tag SNPs, without much loss of power. Depending on the specific haplotype block-partitioning algorithm and the disease model, when the identified tag SNPs are only 25% of all the SNPs, the power is reduced by only 4%, on average, compared with a power loss of approximately 12% when the same number of randomly chosen SNPs is used in a two-locus haplotype analysis. When the identified tag SNPs are approximately 14% of all the SNPs, the power is reduced by approximately 9%, compared with a power loss of approximately 21% when the same number of randomly chosen SNPs is used in a two-locus haplotype analysis. Our study also indicates that haplotype-based analysis can be much more powerful than marker-by-marker analysis.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12439824 o0002-9297 (Print) Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.12439824Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles 90089, USA.~?Zhao, H.2000 Family-based association studies563-87Stat Methods Med Res96Alleles Environment *Epidemiologic Methods Family Health Genetic Markers Genetic Predisposition to Disease/*epidemiology Humans Linkage (Genetics)/*genetics Statistics/*methods X ChromosomeDecOver the past decade, attention has turned from positional cloning of Mendelian disease genes to the dissection of complex diseases. Both theoretical and empirical studies have shown that traditional linkage studies may be inferior in power compared to studies that directly utilize allele status. Case-control association studies, as an alternative, are subject to bias due to population stratification. As a compromise between linkage studies and case-control studies, family-based association designs have received great attention recently due to their potentially higher power to identify complex disease genes and their robustness in the presence of population substructure. In this review, we first describe the basic family-based association design involving one affected offspring with its two parents, all genotyped for a biallelic genetic marker. Extensions of the original transmission disequilibrium tests to multiallelic markers, families with multiple siblings, families with incomplete parental genotypes, and general pedigree structures are discussed. Further developments of statistical methods to study quantitative traits, to analyse genes on the X chromosome, to incorporate multiple tightly linked markers, to identify imprinting genes, and to detect gene-environment interactions are also reviewed. Finally, we discuss the implications of the completion of the Human Genome Project and the identification of hundreds of thousands of genetic polymorphisms on employing family-based association designs to search for complex disease genes.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11308071 n0962-2802 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Review11308071[Yale University School of Medicine, New Haven, Connecticut 06520, USA. hongyu.zhao@yale.edu ~?0Zhao, H. Nettleton, D. Soller, M. Dekkers, J. C.2005Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between markers and QTL77-87 Genet Res861!Alleles Animals Chromosome Mapping Computer Simulation Crosses, Genetic Genetic Markers Genetics, Population Haplotypes Heterozygote Likelihood Functions *Linkage Disequilibrium Models, Genetic Models, Statistical Polymorphism, Genetic Quantitative Trait Loci/*genetics Regression AnalysisAugEffectiveness of marker-assisted selection (MAS) and quantitative trait loci (QTL) mapping using population-wide linkage disequilibrium (LD) between markers and QTL depends on the extent of LD and how it declines with distance in a population. Because marker-QTL LD cannot be observed directly, the objective of this study was to evaluate alternative measures of observable LD between multi-allelic markers as predictors of usable LD of multi-allelic markers with presumed biallelic QTL. Observable LD between marker pairs was evaluated using eight existing measures and one new measure. These consisted of two pooled and standardized measures of LD between pairs of alleles at two markers based on Lewontin's LD measure, two pooled measures of squared correlations between alleles, one standardized measure using Hardy-Weinberg heterozygosities, and four measures based on the chi-square statistic for testing for association between alleles at two loci. In simulated populations with a range of LD generated by drift and a range of marker polymorphism, marker-marker LD measured by a standardized chi-square statistic (denoted chi(2')) was found to be the best predictor of useable marker-QTL LD for a group of multi-allelic markers. Estimates of the level and decline of marker-marker LD with distance obtained from chi(2') were linearly and highly correlated with usable LD of those markers with QTL across population structures and marker polymorphism. Corresponding relationships were poorer for the other marker-marker LD measures. Therefore, when LD is generated by drift, chi(2') is recommended to quantify the amount and extent of usable LD in a population for QTL mapping and MAS based on multi-allelic markers.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16181525 B0016-6723 (Print) Journal Article Research Support, Non-U.S. Gov't16181525ZDepartment of Animal Science, 239 Kildee Hall, Iowa State University, Ames, IA 50011, USA. ~? Zhao, L. P. Li, S. S. Khalid, N.2003A method for the assessment of disease associations with single-nucleotide polymorphism haplotypes and environmental variables in case-control studies1231-50Am J Hum Genet725(Apolipoprotein C-III Apolipoproteins C/genetics Case-Control Studies Computer Simulation Coronary Disease/genetics Coronary Restenosis/genetics Epidemiology, Molecular/*methods *Haplotypes Humans *Linkage (Genetics) Mathematics *Models, Genetic Monte Carlo Method *Polymorphism, Single NucleotideMayzThe rough draft of the human genome map has been used to identify most of the functional genes in the human genome, as well as to identify nucleotide variations, known as "single-nucleotide polymorphisms" (SNPs), in these genes. By use of advanced biotechnologies, researchers are beginning to genotype thousands of SNPs from biological samples. Among the many possible applications, one of them is the study of SNP associations with complex human diseases, such as cancers or coronary heart diseases, by using a case-control study design. Through the gathering of environmental risk factors and other lifestyle factors, such a study can be effectively used to investigate interactions between genes and environmental factors in their associations with disease phenotype. Earlier, we developed a method to statistically construct individuals' haplotypes and to estimate the distribution of haplotypes of multiple SNPs in a defined population, by use of estimating-equation techniques. Extending this idea, we describe here an analytic method for assessing the association between the constructed haplotypes along with environmental factors and the disease phenotype. This method is also robust to the model assumptions and is scalable to a large number of SNPs. Asymptotic properties of estimations in the method are proved theoretically and are tested for finite sample sizes by use of simulations. To demonstrate the use of the method, we applied it to assess the possible association between apolipoprotein CIII (six coding SNPs) and restenosis by using a case-control data set. Our analysis revealed two haplotypes that may reduce the risk of restenosis.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12704570 F0002-9297 (Print) Journal Article Research Support, U.S. Gov't, P.H.S.12704570sDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. lzhao@fhcrc.org 2~?YZhao, X. Li, H. Shi, Y. Tang, R. Chen, W. Liu, J. Feng, G. Shi, J. Yan, L. Liu, H. He, L.2006Significant association between the genetic variations in the 5' end of the N-methyl-D-aspartate receptor subunit gene GRIN1 and schizophrenia747-53Biol Psychiatry598x5' Flanking Region/*genetics Adult Asian Continental Ancestry Group Carrier Proteins/*genetics Case-Control Studies Demography Family Health Female Gene Frequency *Genetic Predisposition to Disease Genotype Humans Male Middle Aged Nerve Tissue Proteins/*genetics Receptors, N-Methyl-D-Aspartate/*genetics Schizophrenia/*genetics Statistics, Nonparametric *Variation (Genetics)Apr 15BACKGROUND: N-methyl-D-aspartate (NMDA) receptors play important roles in many neurophysiological processes. Evidence from previous studies indicate that NMDA receptors contribute to the pathophysiology of schizophrenia. Two NMDA receptor subunit genes, GRIN1 and GRIN2A, are both good candidate genes for schizophrenia. METHOD: We genotyped five single nucleotide polymorphisms (SNPs) in GRIN1 and two in GRIN2A in 2455 Han Chinese subjects, including population- and family-based samples, and performed case-control and transmission disequilibrium test (TDT) analyses. A microsatellite in GRIN2A was genotyped in population-based samples and a Mann-Whitney U test was performed. RESULTS: A highly significant association was detected at the 5' end of GRIN1. Analyses of single variants and multiple-locus haplotypes indicate that the association is mainly generated by rs11146020 (case-control study: p = .0000013, odds ratio = .61, 95% confidence interval .50-.74; TDT: p = .0019, T/NT = 79/123). No association was found in the GRIN2A polymorphisms. CONCLUSIONS: Our results provide support for the hypothesis that NMDA receptors are an important factor in schizophrenia. Moreover, rs11146020 is located in 5' untranslated region where several functional elements have been found. Hence, the SNP is a potential candidate in altering risk for schizophrenia and worthy of further replication and functional study.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16476413 T0006-3223 (Print) Comparative Study Journal Article Research Support, Non-U.S. Gov't164764133Bio-X Center, Shanghai Jiao Tong University, China.-~?'Zheng, G. Freidlin, B. Gastwirth, J. L.2006.Robust genomic control for association studies350-6Am J Hum Genet782FebRPopulation-based case-control studies are a useful method to test for a genetic association between a trait and a marker. However, the analysis of the resulting data can be affected by population stratification or cryptic relatedness, which may inflate the variance of the usual statistics, resulting in a higher-than-nominal rate of false-positive results. One approach to preserving the nominal type I error is to apply genomic control, which adjusts the variance of the Cochran-Armitage trend test by calculating the statistic on data from null loci. This enables one to estimate any additional variance in the null distribution of statistics. When the underlying genetic model (e.g., recessive, additive, or dominant) is known, genomic control can be applied to the corresponding optimal trend tests. In practice, however, the mode of inheritance is unknown. The genotype-based chi (2) test for a general association between the trait and the marker does not depend on the underlying genetic model. Since this general association test has 2 degrees of freedom (df), the existing formulas for estimating the variance factor by use of genomic control are not directly applicable. By expressing the general association test in terms of two Cochran-Armitage trend tests, one can apply genomic control to each of the two trend tests separately, thereby adjusting the chi (2) statistic. The properties of this robust genomic control test with 2 df are examined by simulation. This genomic control-adjusted 2-df test has control of type I error and achieves reasonable power, relative to the optimal tests for each model.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16400614 !0002-9297 (Print) Journal Article16400614Office of Biostatistics Research, Division of Epidemiology and Clinical Applications, National Heart, Lung and Blood Institute, Bethesda, MD, USA. zhengg@nhlbi.nih.gov.~?Zheng, G. Tian, X.2005UThe impact of diagnostic error on testing genetic association in case-control studies869-82Stat Med246*Case-Control Studies *Diagnostic Errors Genetic Predisposition to Disease Humans *Models, Genetic *Models, Statistical Sensitivity and SpecificityMar 30rIn case-control studies, subjects in the case group may be recruited from suspected patients who are diagnosed positively with disease. While many statistical methods have been developed for measurement error or misclassification of exposure variables in epidemiological studies, no studies have been reported on the effect of errors in diagnosing disease on testing genetic association in case-control studies. We study the impact of using the original Cochran-Armitage trend test assuming no diagnostic error when, in fact, cases and controls may be clinically diagnosed by an imperfect gold standard or a reference test. The type I error, sample size and asymptotic power of trend tests are examined under a family of genetic models in the presence of diagnostic error. The empirical powers of the trend tests are also compared by simulation studies under various genetic models.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15568195 !0277-6715 (Print) Journal Article15568195yOffice of Biostatistics Research, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA. zhengg@nhlbi.nih.gov ~?Zhu, G. Evans, D. M. Duffy, D. L. Montgomery, G. W. Medland, S. E. Gillespie, N. A. Ewen, K. R. Jewell, M. Liew, Y. W. Hayward, N. K. Sturm, R. A. Trent, J. M. Martin, N. G.2004bA genome scan for eye color in 502 twin families: most variation is due to a QTL on chromosome 15q197-210Twin Res72Chromosomes, Human, Pair 15/*genetics Eye Color/*genetics Female Humans Linkage (Genetics)/*genetics Male Quantitative Trait Loci/*genetics Twins/*genetics Variation (Genetics)/geneticsAprWe have rated eye color on a 3-point scale (1 = blue/grey, 2 = hazel/green, 3 = brown) in 502 twin families and carried out a 5-10 cM genome scan (400-757 markers). We analyzed eye color as a threshold trait and performed multipoint sib pair linkage analysis using variance components analysis in Mx. A lod of 19.2 was found at the marker D15S1002, less than 1 cM from OCA2, which has been previously implicated in eye color variation. We estimate that 74% of variance in eye color liability is due to this QTL and a further 18% due to polygenic effects. However, a large shoulder on this peak suggests that other loci affecting eye color may be telomeric of OCA2 and inflating the QTL estimate. No other peaks reached genome-wide significance, although lods > 2 were seen on 5p and 14q and lods >1 were additionally seen on chromosomes 2, 3, 6, 7, 8, 9, 17 and 18. Most of these secondary peaks were reduced or eliminated when we repeated the scan as a two locus analysis with the 15q linkage included, although this does not necessarily exclude them as false positives. We also estimated the interaction between the 15q QTL and the other marker locus but there was only minor evidence for additive x additive epistasis. Elaborating the analysis to the full two-locus model including non-additive main effects and interactions did not strengthen the evidence for epistasis. We conclude that most variation in eye color in Europeans is due to polymorphism in OCA2 but that there may be modifiers at several other loci.fhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15169604 g1369-0523 (Print) Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.15169604>Queensland Institute of Medical Research, Brisbane, Australia.