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Principal component analysis of genetic data

Nature Genetics volume 40, pages 491–492 (2008)Cite this article

Principal component analysis (PCA) has been a useful tool for analysis of genetic data, particularly in studies of human migration. A new study finds evidence that the observed geographic gradients, traditionally thought to represent major historical migrations, may in fact have other interpretations.

Principal component analysis (PCA) has been used for several decades to study human population migrations, resulting in remarkable inferences about history. On page 646 of this issue, John Novembre and Matthew Stephens1 show that the geographic gradients that emerge when PCA is applied to genetic data—and that are sometimes interpreted as highly suggestive of major historical migrations—can also have other explanations. We suggest guidelines for scientists interested in using PCA in genetic analysis in light of this potential concern and highlight three applications in which PCA has continued value: detecting population substructure, correcting for stratification in disease studies and making qualified inferences about human history.

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Figure 1: PCA continues to provide evidence of important migration events.

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Authors and Affiliations

  1. David Reich and Alkes L. Price are at the Department of Genetics, Harvard Medical School, New Research Building, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA.,

    David Reich & Alkes L Price

  2. Nick Patterson is at the Broad Institute of Harvard and the Massachusetts Institute of Technology, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA. reich@genetics.med.harvard.edu,

    Nick Patterson

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  1. David Reich
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  2. Alkes L Price
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Reich, D., Price, A. & Patterson, N. Principal component analysis of genetic data. Nat Genet 40, 491–492 (2008). https://doi.org/10.1038/ng0508-491

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