Genome-wide efficient mixed-model analysis for association studies

• Published in: Nature genetics Vol. 44; no. 7; pp. 821 - 824
• Main Authors: Zhou, Xiang, Stephens, Matthew
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.07.2012; Nature Publishing Group
• Subjects: 631/114/2397; 631/208/205/2138; Agriculture; Algorithms; Animal Genetics and Genomics; Biological and medical sciences; Biomedical and Life Sciences; Biomedicine; Cancer Research; Computer Simulation; Fundamental and applied biological sciences. Psychology; Gemma; Gene Function; Genetics of eukaryotes. Biological and molecular evolution; Genome, Human; Genome-Wide Association Study - methods; Human Genetics; Humans; Linear Models; Models, Genetic; Polymorphism, Single Nucleotide; Population Groups - genetics; Single nucleotide polymorphisms; Software; Studies; technical-report; United States; Models; Association
• ISSN: 1061-4036; 1546-1718; 1546-1718
• DOI: 10.1038/ng.2310

Matthew Stephens and Xiang Zhou report an efficient exact method for accounting for population stratification and relatedness in genome-wide association analyses. Their method, genome-wide efficient mixed-model association (GEMMA) is implemented in freely available software. Linear mixed models have attracted considerable attention recently as a powerful and effective tool for accounting for population stratification and relatedness in genetic association tests. However, existing methods for exact computation of standard test statistics are computationally impractical for even moderate-sized genome-wide association studies. To address this issue, several approximate methods have been proposed. Here, we present an efficient exact method, which we refer to as genome-wide efficient mixed-model association (GEMMA), that makes approximations unnecessary in many contexts. This method is approximately n times faster than the widely used exact method known as efficient mixed-model association (EMMA), where n is the sample size, making exact genome-wide association analysis computationally practical for large numbers of individuals.