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Lessons learned from Genetic Analysis Workshop 17: transitioning from genome‐wide association studies to whole‐genome statistical genetic analysis
Author(s) -
Wilson Alexander F.,
Ziegler Andreas
Publication year - 2011
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20659
Subject(s) - heritability , type i and type ii errors , biology , genetics , genetic association , population , genome wide association study , locus (genetics) , sample size determination , trait , statistics , genetic model , genotype , mathematics , demography , single nucleotide polymorphism , computer science , gene , sociology , programming language
Abstract Genetic Analysis Workshop 17 (GAW17) focused on the transition from genome‐wide association study designs and methods to the study designs and statistical genetic methods that will be required for the analysis of next‐generation sequence data including both common and rare sequence variants. In the 166 contributions to GAW17, a wide variety of statistical methods were applied to simulated traits in population‐ and family‐based samples, and results from these analyses were compared to the known generating model. In general, many of the statistical genetic methods used in the population‐based sample identified causal sequence variants (SVs) when the estimated locus‐specific heritability, as measured in the population‐based sample, was greater than about 0.08. However, SVs with locus‐specific heritabilities less than 0.03 were rarely identified consistently. In the family‐based samples, many of the methods detected SVs that were rarer than those detected in the population‐based sample, but the estimated locus‐specific heritabilities for these rare SVs, as measured in the family‐based samples, were substantially higher (>0.2) than their corresponding heritabilities in the population‐based samples. Substantial inflation of the type I error rate was observed across a wide variety of statistical methods. Although many of the contributions found little inflation in type I error for Q4, a trait with no causal SVs, type I error rates for Q1 and Q2 were well above their nominal levels with the inflation for Q1 being higher than that for Q2. It seems likely that this inflation in type I error is due to correlations among SVs. Genet. Epidemiol . 35:S107–S114, 2011. © 2011 Wiley Periodicals, Inc.