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Principal components analysis corrects for stratification in genome-wide association studies

Nature Genetics volume 38, pages 904–909 (2006)Cite this article

Abstract

Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.

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Figure 1: The EIGENSTRAT algorithm, illustrated on simulated data.
Figure 2: The top two axes of variation of European American samples.

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Acknowledgements

The authors are grateful to B. Blumenstiel, M. DeFelice, M. Parkin, R. Barry, W. Winslow, C. Healy and S. Gabriel for generation of the Affymetrix genotype data. We are grateful to the BRASS study participants, the BRASS study team, and our rheumatology colleagues at the Brigham and Women's Hospital Arthritis Center. We thank C. Campbell and J. Hirschhorn for helpful comments and sharing data from their paper6. The BRASS study was supported by a grant from Millennium Pharmaceuticals. D.R. is supported in part by a Burroughs Wellcome Career Development Award in the Biomedical Sciences.

Author information

Authors and Affiliations

  1. Department of Genetics, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Alkes L Price & David Reich

  2. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, 02142, Massachusetts, USA

    Alkes L Price, Nick J Patterson, Robert M Plenge & David Reich

  3. Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, 02115, Massachusetts, USA

    Robert M Plenge, Michael E Weinblatt & Nancy A Shadick

Authors
  1. Alkes L Price
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  5. Nancy A Shadick
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  6. David Reich
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Corresponding author

Correspondence to Alkes L Price.

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Competing interests

M.E.W. serves as a consultant to Millennium Pharmaceuticals; the BRASS study, which produced a data set described in the paper, was supported by a grant from Millenium Pharmaceuticals.

Supplementary information

Supplementary Fig. 1

P-P plot of EIGENSTRAT test statistics. (PDF 429 kb)

Supplementary Table 1

Simulations using K axes of variation. (PDF 58 kb)

Supplementary Table 2

Simulations using M SNPs. (PDF 66 kb)

Supplementary Table 3

Simulations of Pritchard and Donnelly. (PDF 68 kb)

Supplementary Table 4

Simulations with no stratification and n subpopulations. (PDF 73 kb)

Supplementary Table 5

Stratification correction at rs10511418 using M SNPs. (PDF 73 kb)

Supplementary Note (PDF 207 kb)

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Price, A., Patterson, N., Plenge, R. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, 904–909 (2006). https://doi.org/10.1038/ng1847

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