Efficient multivariate linear mixed model algorithms for genome-wide association studies

Nature Methods volume 11, pages407–409(2014)Cite this article

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Abstract

Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations between single-nucleotide polymorphisms and multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests. These algorithms offer improved computation speed, power and P-value calibration over existing methods, and can deal with more than two phenotypes.

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Figure 1: Statistical benefits of the mvLMM algorithm implemented in GEMMA.

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Acknowledgements

This research was supported in part by US National Institutes of Health (NIH) grant HL092206 (principal investigator, Y. Gilad) and NIH grant HG02585 to M.S. We thank A.J. Lusis for making the mouse genotype and phenotype data available, and the NFBC1966 Study Investigators for making the NFBC1966 data available. The NFBC1966 study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with the Broad Institute, University of California Los Angeles, University of Oulu, and the National Institute for Health and Welfare in Finland. This manuscript was not prepared in collaboration with investigators of the NFBC1966 study and does not necessarily reflect their views or those of their host institutions.

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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

    Xiang Zhou & Matthew Stephens

Authors
  1. Xiang Zhou
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  2. Matthew Stephens
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Contributions

X.Z. and M.S. conceived the idea and designed the study. X.Z. developed the algorithms, implemented the software and performed the analyses. X.Z. and M.S. wrote the paper.

Corresponding authors

Correspondence to Xiang Zhou or Matthew Stephens.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8, Supplementary Tables 1 and 2, and Supplementary Note (PDF 1889 kb)

Supplementary Software

GEMMA version 0.94. (ZIP 24880 kb)

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Zhou, X., Stephens, M. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods 11, 407–409 (2014). https://doi.org/10.1038/nmeth.2848

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