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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Bibliographic Details
Published in:Nature genetics Vol. 42; no. 4; pp. 348 - 354
Main Authors: Kang, Hyun Min, Sul, Jae Hoon, Service, Susan K, Zaitlen, Noah A, Kong, Sit-yee, Freimer, Nelson B, Sabatti, Chiara, Eskin, Eleazar
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.04.2010
Nature Publishing Group
Subjects:
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
Online Access:Get full text
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Summary: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.
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These authors contributed equally to this work.
ISSN:1061-4036
1546-1718
1546-1718
DOI:10.1038/ng.548