Assessing the impact of population stratification on genetic association studies

Matthew L. Freedman, David Reich, Kathryn L. Penney, Gavin J. McDonald, Andre A. Mignault, Nick Patterson, Stacey B. Gabriel, Eric J. Topol, Jordan W. Smoller, Carlos N. Pato, Michele T. Pato, Tracey L. Petryshen, Laurence N. Kolonel, Eric S. Lander, Pamela Sklar, Brian Henderson, Joel N. Hirschhorn, David Altshuler

Research output: Contribution to journalArticlepeer-review

660 Scopus citations

Abstract

Population stratification refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry rather than association of genes with disease. It has been proposed that false positive associations due to stratification can be controlled by genotyping a few dozen unlinked genetic markers. To assess stratification empirically, we analyzed data from 11 case-control and case-cohort association studies. We did not detect statistically significant evidence for stratification but did observe that assessments based on a few dozen markers lack power to rule out moderate levels of stratification that could cause false positive associations in studies designed to detect modest genetic risk factors. After increasing the number of markers and samples in a case-cohort study (the design most immune to stratification), we found that stratification was in fact present. Our results suggest that modest amounts of stratification can exist even in well designed studies.

Original languageEnglish
Pages (from-to)388-393
Number of pages6
JournalNature Genetics
Volume36
Issue number4
DOIs
StatePublished - Apr 2004
Externally publishedYes

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