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Matching on Race and Ethnicity in Case-Control Studies as a Means of Control for Population Stratification

Anand P. Chokkalingam, Melinda C. Aldrich, Karen Bartley, Ling-I Hsu, Catherine Metayer, Lisa F. Barcellos, Joseph L. Wiemels, John K. Wiencke, Patricia A. Buffler and Steve Selvin

Some investigators argue that controlling for self-reported race or ethnicity, either in statistical analysis or in study design, is sufficient to mitigate unwanted influence from population stratification. In this report, we evaluated the effectiveness of a study design involving matching on self-reported ethnicity and race in minimizing bias due to population stratification within an ethnically admixed population in California. We estimated individual genetic ancestry using structured association methods and a panel of ancestry informative markers, and observed no statistically significant difference in distribution of genetic ancestry between cases and controls (P=0.46). Stratification by Hispanic ethnicity showed similar results. We evaluated potential confounding by genetic ancestry after adjustment for race and ethnicity for 1260 candidate gene SNPs, and found no major impact (>10%) on risk estimates. In conclusion, we found no evidence of confounding of genetic risk estimates by population substructure using this matched design. Our study provides strong evidence supporting the race- and ethnicity-matched case-control study design as an effective approach to minimize systematic bias due to differences in genetic ancestry between cases and controls.