FaST-LMM-Select for addressing confounding from spatial structure and rare variants (original) (raw)

Nature Genetics volume 45, pages 470–471 (2013)Cite this article

Subjects

To the Editor:

A recent report by Mathieson and McVean1 showed that confounding in genome-wide association studies (GWAS) resulting from spatially structured populations in conjunction with rare variants could not be corrected by currently available statistical genetics methods. In particular, when simulating that the non-genetic cause of disease arose from a sharply defined spatial region, genomic control2, principal-component analysis (PCA)3 and linear mixed models (LMMs)4,5 all failed to correct for stratification, resulting in systematically inflated test statistics1. Although several research avenues were proposed as possible solutions to the problem1, none has so far been shown to work. Additionally, it was speculated that any method that could correct for such confounding would require fine-grained geographic information1.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Subscribe to this journal

Receive 12 print issues and online access

$259.00 per year

only $21.58 per issue

Buy this article

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Additional access options:

Figure 1: Comparison of three methods for genome-wide association analyses in the presence of confounding due to spatial structure and rare variants.

References

  1. Mathieson, I. & McVean, G. Nat. Genet. 44, 243–246 (2012).
    Article CAS Google Scholar
  2. Devlin, B. & Roeder, K. Biometrics 55, 997–1004 (1999).
    Article CAS Google Scholar
  3. Price, A.L. et al. Nat. Genet. 38, 904–909 (2006).
    Article CAS Google Scholar
  4. Kang, H.M. et al. Nat. Genet. 42, 348–354 (2010).
    Article CAS Google Scholar
  5. Lippert, C. et al. Nat. Methods 8, 833–835 (2011).
    Article CAS Google Scholar
  6. Listgarten, J. et al. Nat. Methods 9, 525–526 (2012).
    Article CAS Google Scholar
  7. Lippert, C., Quon, G., Listgarten, J. & Heckerman, D. Sci. Rep. (in the press).
  8. Hayes, B.J., Visscher, P.M. & Goddard, M.E. Genet. Res. 91, 47–60 (2009).
    Article CAS Google Scholar
  9. Yu, J. et al. Nat. Genet. 38, 203–208 (2006).
    Article CAS Google Scholar
  10. Agresti, A. Categorical Data Analysis (Wiley, New York, 2002).
    Book Google Scholar

Download references

Author information

Author notes

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

Authors and Affiliations

  1. Microsoft Research, Los Angeles, California, USA
    Jennifer Listgarten, Christoph Lippert & David Heckerman

Authors

  1. Jennifer Listgarten
  2. Christoph Lippert
  3. David Heckerman

Corresponding authors

Correspondence toJennifer Listgarten, Christoph Lippert or David Heckerman.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

About this article

Cite this article

Listgarten, J., Lippert, C. & Heckerman, D. FaST-LMM-Select for addressing confounding from spatial structure and rare variants.Nat Genet 45, 470–471 (2013). https://doi.org/10.1038/ng.2620

Download citation

This article is cited by