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Genome-wide efficient mixed-model analysis for association studies

Nature Genetics volume 44, pages 821–824 (2012)Cite this article

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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.

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Figure 1: Comparison of GEMMA with EMMA, EMMAX and GRAMMAR on HMDP HDL-C data and WTCCC Crohn's disease data.

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Acknowledgements

This research is supported in part by grants from the US National Institutes of Health (NIH) (HL092206 to Y. Gilad and HG02585 to M.S.). We thank A.J. Lusis for making the mouse genotype and phenotype data available. This study also makes use of data generated by the WTCCC15. A full list of the investigators who contributed to the generation of the data is available from the WTCCC website. Funding for the WTCCC project was provided by the Wellcome Trust (award 085475).

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Authors and Affiliations

  1. Department of Human Genetics, University of Chicago, Chicago, Illinois, USA

    Xiang Zhou & Matthew Stephens

  2. Department of Statistics, University of Chicago, Chicago, Illinois, USA

    Matthew Stephens

Authors
  1. Xiang Zhou
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  2. Matthew Stephens
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Contributions

X.Z. and M.S. designed the study, developed methods and wrote the manuscript. X.Z. implemented software and analyzed data.

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Correspondence to Xiang Zhou or Matthew Stephens.

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The authors declare no competing financial interests.

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Supplementary Figures 1 and 2, Supplementary Table 1 and Supplementary Note (PDF 365 kb)

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Zhou, X., Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44, 821–824 (2012). https://doi.org/10.1038/ng.2310

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