Technical Report | Published:

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

Nature Genetics volume 44, pages 11661170 (2012) | Download Citation

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    , , , & An Icelandic example of the impact of population structure on association studies. Nat. Genet. 37, 90–95 (2005).

  2. 2.

    & Population structure and cryptic relatedness in genetic association studies. Stat. Sci. 24, 451–471 (2009).

  3. 3.

    The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinb. 52, 399–433 (1918).

  4. 4.

    Estimation of variance and covariance components. Biometrics 9, 226–252 (1953).

  5. 5.

    , & The use of measured genotype information in the analysis of quantitative phenotypes in man. I. Models and analytical methods. Ann. Hum. Genet. 50, 181–194 (1986).

  6. 6.

    et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208 (2006).

  7. 7.

    et al. Efficient control of population structure in model organism association mapping. Genetics 178, 1709–1723 (2008).

  8. 8.

    et al. FaST linear mixed models for genome-wide association studies. Nat. Methods 8, 833–835 (2011).

  9. 9.

    & Family-based association tests for genomewide association scans. Am. J. Hum. Genet. 81, 913–926 (2007).

  10. 10.

    et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).

  11. 11.

    et al. Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42, 355–360 (2010).

  12. 12.

    , & Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics 177, 577–585 (2007).

  13. 13.

    , & A genomic background based method for association analysis in related individuals. PLoS ONE 2, e1274 (2007).

  14. 14.

    et al. The effect of genetic drift in a young genetically isolated population. Ann. Hum. Genet. 69, 288–295 (2005).

  15. 15.

    et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465, 627–631 (2010).

  16. 16.

    et al. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).

  17. 17.

    & Genomic control for association studies. Biometrics 55, 997–1004 (1999).

  18. 18.

    , & Association studies for quantitative traits in structured populations. Genet. Epidemiol. 22, 78–93 (2002).

  19. 19.

    Population Structure and Cryptic Relatedness in Genetic Association Studies. PhD Thesis, University of London (2009).

Download references

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-).

Author information

Affiliations

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

Authors

  1. Search for Gulnara R Svishcheva in:

  2. Search for Tatiana I Axenovich in:

  3. Search for Nadezhda M Belonogova in:

  4. Search for Cornelia M van Duijn in:

  5. Search for Yurii S Aulchenko in:

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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Yurii S Aulchenko.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Tables 1–6, Supplementary Figures 1 and 2 and Supplementary Note

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ng.2410

Further reading