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      Variance component model to account for sample structure in genome-wide association studies.

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          Abstract

          Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.

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          Author and article information

          Journal
          Nat Genet
          Nature genetics
          Springer Science and Business Media LLC
          1546-1718
          1061-4036
          Apr 2010
          : 42
          : 4
          Affiliations
          [1 ] Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
          Article
          ng.548 NIHMS196426
          10.1038/ng.548
          3092069
          20208533
          cb63718c-1125-4e18-8548-c6b4f91e7610
          History

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