Nature Genetics 38, 904 - 909 (2006)
Published online: 23 July 2006; | doi:10.1038/ng1847
Principal components analysis corrects for stratification in genome-wide association studiesAlkes L Price1, 2, Nick J Patterson2, Robert M Plenge2, 3, Michael E Weinblatt3, Nancy A Shadick3 & David Reich1, 21
Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA. 2
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA. 3
Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.
Correspondence should be addressed to Alkes L Price aprice@broad.mit.edu 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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