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

Eleazar Eskin and colleagues report a variance component model for correcting for sample structure in association studies. The EMMAX program is publicly available and may be used for analysis of genome-wide association study datasets. Although genome-wide association studies (GWASs) have identified...

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Vydáno v:	Nature genetics Ročník 42; číslo 4; s. 348 - 354
Hlavní autoři:	Kang, Hyun Min, Sul, Jae Hoon, Service, Susan K, Zaitlen, Noah A, Kong, Sit-yee, Freimer, Nelson B, Sabatti, Chiara, Eskin, Eleazar
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Nature Publishing Group US 01.04.2010 Nature Publishing Group
Témata:	631/1647/2217/2138 Agriculture Animal Genetics and Genomics Biomedical and Life Sciences Biomedicine Cancer Research Gene Function Genetics Genome-Wide Association Study Genomics Genotype & phenotype Human Genetics Humans Identification and classification Methods Models, Genetic Models, Statistical Physiological aspects Polymorphism, Single Nucleotide Population Groups - genetics Principal Component Analysis Principal components analysis Quantitative Trait Loci Software Studies technical-report United States
ISSN:	1061-4036, 1546-1718, 1546-1718
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Shrnutí:	Eleazar Eskin and colleagues report a variance component model for correcting for sample structure in association studies. The EMMAX program is publicly available and may be used for analysis of genome-wide association study datasets. 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors contributed equally to this work.
ISSN:	1061-4036 1546-1718 1546-1718
DOI:	10.1038/ng.548