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Rapid variance components–based method for whole-genome association analysis

Nature Genetics volume 44, pages 1166–1170 (2012)Cite this article

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Abstract

The variance component tests used in genome-wide association studies (GWAS) including large sample sizes become computationally exhaustive when the number of genetic markers is over a few hundred thousand. We present an extremely fast variance components–based two-step method, GRAMMAR-Gamma, developed as an analytical approximation within a framework of the score test approach. Using simulated and real human GWAS data sets, we show that this method provides unbiased estimates of the SNP effect and has a power close to that of the likelihood ratio test–based method. The computational complexity of our method is close to its theoretical minimum, that is, to the complexity of the analysis that ignores genetic structure. The running time of our method linearly depends on sample size, whereas this dependency is quadratic for other existing methods. Simulations suggest that GRAMMAR-Gamma may be used for association testing in whole-genome resequencing studies of large human cohorts.

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Figure 1: Comparison of mixed model–based methods.
Figure 2: Correspondence between GRAMMAR-Gamma and FASTA test statistics.
Figure 3: Run time on a single processor for different two-step methods, namely, EMMAX, mmscore realization of FASTA, FaST-LMM and GRAMMAR-Gamma.

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Acknowledgements

We thank A. Kirichenko, D. Fabregat Traver and P. Bientinesi for technical support and advice and M. Axenovich, D. Balding, P. Borodin and W. Astle for discussion. This work was supported by grants from the Russian Foundation for Basic Research (RFBR) Programs of the Russian Academy of Sciences and the RFBR-Helmholtz Joint Research Groups program (research project 12-04-91322-).

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Authors and Affiliations

  1. Siberian Division of the Russian Academy of Sciences, Institute of Cytology and Genetics, Novosibirsk, Russia

    Gulnara R Svishcheva, Tatiana I Axenovich, Nadezhda M Belonogova & Yurii S Aulchenko

  2. Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands

    Cornelia M van Duijn

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  1. Gulnara R Svishcheva
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  2. Tatiana I Axenovich
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  3. Nadezhda M Belonogova
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  5. Yurii S Aulchenko
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Contributions

G.R.S. developed the GRAMMAR-Gamma statistical test, ran the simulations and analyzed the simulated data. N.M.B. analyzed human and A. thaliana data and designed figures and tables. C.M.v.D. provided the human data and supervised its analyses. T.I.A. and Y.S.A. jointly designed and supervised the project and wrote the paper. All authors contributed to critical review of the manuscript during its preparation.

Corresponding author

Correspondence to Yurii S Aulchenko.

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The authors declare no competing financial interests.

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Supplementary Tables 1–6, Supplementary Figures 1 and 2 and Supplementary Note (PDF 797 kb)

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Svishcheva, G., Axenovich, T., Belonogova, N. et al. Rapid variance components–based method for whole-genome association analysis. Nat Genet 44, 1166–1170 (2012). https://doi.org/10.1038/ng.2410

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