FaST linear mixed models for genome-wide association studies

Nature Methods volume 8, pages833–835(2011)Cite this article

Subjects

Abstract

We describe factored spectrally transformed linear mixed models (FaST-LMM), an algorithm for genome-wide association studies (GWAS) that scales linearly with cohort size in both run time and memory use. On Wellcome Trust data for 15,000 individuals, FaST-LMM ran an order of magnitude faster than current efficient algorithms. Our algorithm can analyze data for 120,000 individuals in just a few hours, whereas current algorithms fail on data for even 20,000 individuals (http://mscompbio.codeplex.com/).

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

Rent or Buy

All prices are NET prices.

Figure 1: Computational costs of FaST-LMM and EMMAX.
Figure 2: Accuracy of association P values resulting from SNP sampling on WTCCC data for the Crohn's disease phenotype.

References

  1. 1

    Balding, D.J. Nat. Rev. Genet. 7, 781–791 (2006).

    CAS  Article  Google Scholar 

  2. 2

    Yu, J. et al. Nat. Genet. 38, 203–208 (2006).

    CAS  Article  Google Scholar 

  3. 3

    Kang, H.M. et al. Genetics 107, 1709–1723 (2008).

  4. 4

    Zhang, Z. et al. Nat. Genet. 42, 355–360 (2010).

    CAS  Article  Google Scholar 

  5. 5

    Kang, H.M. et al. Nat. Genet. 42, 348–354 (2010).

    CAS  Article  Google Scholar 

  6. 6

    Zhao, K. et al. PLoS Genet. 3, e4 (2007).

    Article  Google Scholar 

  7. 7

    Price, A.L., Zaitlen, N.A., Reich, D. & Patterson, N. Nat. Rev. Genet. 11, 459–463 (2010).

    CAS  Article  Google Scholar 

  8. 8

    Henderson, C.R. Applications of Linear Models in Animal Breeding (University of Guelph, Guelph, Ontario, Canada, 1984).

  9. 9

    Goddard, M.E., Wray, N., Verbyla, K. & Visscher, P.M. Stat. Sci. 24, 517–529 (2009).

    Article  Google Scholar 

  10. 10

    Hayes, B.J., Visscher, P.M. & Goddard, M.E. Genet. Res. 91, 47–60 (2009).

    CAS  Article  Google Scholar 

  11. 11

    Fisher, R. Trans. R. Soc. Edinb. 52, 399–433 (1918).

    Article  Google Scholar 

  12. 12

    Yang, J. et al. Nat. Genet. 42, 565–569 (2010).

    CAS  Article  Google Scholar 

  13. 13

    Welham, S. & Thompson, R. J. R. Stat. Soc. B 59, 701–714 (1997).

    Article  Google Scholar 

  14. 14

    Demidenko, E. Mixed Models Theory and Applications (Wiley, Hoboken, New Jersey, USA, 2004).

  15. 15

    Listgarten, J., Kadie, C., Schadt, E.E. & Heckerman, D. Proc. Natl. Acad. Sci. USA 107, 16465–16470 (2010).

    CAS  Article  Google Scholar 

  16. 16

    Devlin, B. & Roeder, K. Biometrics 55, 997–1004 (1999).

    CAS  Article  Google Scholar 

  17. 17

    Edenberg, H.J. et al. BMC Genet. 6 (suppl. 1), S2 (2005).

    Article  Google Scholar 

  18. 18

    Wellcome Trust Case Control Consortium. Nature 447, 661–678 (2007).

Download references

Acknowledgements

We thank E. Renshaw for help with implementation of Brent's method and the χ2 distribution function, J. Carlson for help with tools used to manage the data and deploy runs on our computer cluster, and N. Pfeifer for an implementation of the ATT. A full list of the investigators who contributed to the generation of the Wellcome Trust Case-Control Consortium data we used in this study is available from http://www.wtccc.org.uk/. Funding for the project was provided by the Wellcome Trust (076113 and 085475). The GAW14 data were provided by the members of the Collaborative Study on the Genetics of Alcoholism (US National Institutes of Health grant U10 AA008401).

Author information

Author notes

  1. Christoph Lippert, Jennifer Listgarten and David Heckerman: These authors contributed equally to this work.

Affiliations

  1. Microsoft Research, Los Angeles, California, USA

    Christoph Lippert, Jennifer Listgarten, Ying Liu, Carl M Kadie, Robert I Davidson & David Heckerman

  2. Max Planck Institutes Tübingen, Tübingen, Germany

    Christoph Lippert

Authors
  1. Christoph Lippert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jennifer Listgarten
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carl M Kadie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Robert I Davidson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David Heckerman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.L., J.L. and D.H. designed and performed research, contributed analytic tools, analyzed data and wrote the paper. Y.L. designed and performed research. C.M.K. and R.I.D contributed analytic tools.

Corresponding authors

Correspondence to Christoph Lippert or Jennifer Listgarten or David Heckerman.

Ethics declarations

Competing interests

C.L., J.L., C.M.K., R.I.D. and D.H. are employees of Microsoft. Y.L. was employed by Microsoft while performing this research.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1, Supplementary Notes 1–2 (PDF 394 kb)

Supplementary Software 1

FaST-LMM software and associated files. (ZIP 104264 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lippert, C., Listgarten, J., Liu, Y. et al. FaST linear mixed models for genome-wide association studies. Nat Methods 8, 833–835 (2011). https://doi.org/10.1038/nmeth.1681

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing