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Nat Genet. 2008 May;40(5):646-9. doi: 10.1038/ng.139. Epub 2008 Apr 20.

Interpreting principal component analyses of spatial population genetic variation.

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1
Department of Human Genetics, University of Chicago, 920 E. 58th Street, CLSC 5th floor, Chicago, Illinois 60637, USA.

Abstract

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 regions. They interpreted gradient and wave patterns in these maps as signatures of specific migration events. These interpretations have been controversial, but influential, and the use of PCA has become widespread in analysis of population genetics data. 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.

PMID:
18425127
PMCID:
PMC3989108
DOI:
10.1038/ng.139
[Indexed for MEDLINE]
Free PMC Article
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