Letter abstract


Nature Genetics 40, 646 - 649 (2008)
Published online: 20 April 2008 | doi:10.1038/ng.139

Interpreting principal component analyses of spatial population genetic variation

John Novembre1,3 & Matthew Stephens1,2

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Nearly 30 years ago, Cavalli-Sforza et al. pioneered the use of principal component analysis (PCA) in population genetics and used PCA to produce maps summarizing human genetic variation across continental regions1. They interpreted gradient and wave patterns in these maps as signatures of specific migration events1, 2, 3. These interpretations have been controversial4, 5, 6, 7, but influential8, and the use of PCA has become widespread in analysis of population genetics data9, 10, 11, 12, 13. However, the behavior of PCA for genetic data showing continuous spatial variation, such as might exist within human continental groups, has been less well characterized. Here, we find that gradients and waves observed in Cavalli-Sforza et al.'s maps resemble sinusoidal mathematical artifacts that arise generally when PCA is applied to spatial data, implying that the patterns do not necessarily reflect specific migration events. Our findings aid interpretation of PCA results and suggest how PCA can help correct for continuous population structure in association studies.

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  1. Department of Human Genetics, University of Chicago, 920 E. 58th Street, CLSC 5th floor, Chicago, Illinois 60637, USA.
  2. Department of Statistics, University of Chicago, 5734 S. University Ave., Chicago, Illinois 60637, USA.
  3. Present address: Department of Ecology and Evolutionary Biology, University of California Los Angeles, 621 Charles E. Young Dr. South, Los Angeles, California 90095, USA.

Correspondence to: Matthew Stephens1,2 e-mail: stephens@galton.uchicago.edu



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