Genome-wide efficient mixed-model analysis for association studies

Abstract

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.

Figure 1

Comparison of -log₁₀ p values obtained from GEMMA with those from EMMA (a, b), and EMMAX and GRAMMAR (c, d). In (a) and (b) the p values are shown for the top 10,000 markers and top 100 markers respectively. In (c) and (d) the p values are shown for all markers (1.9 million and 442k respectively).