~?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 Mo