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1 Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics and 2 Abramson Cancer Center, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
Requests for reprints: Timothy R. Rebbeck, Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, 904 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021. Phone: 215-898-1793; Fax: 215-573-2265. E-mail: trebbeck{at}cceb.med.upenn.edu
Confounding by ethnicity (i.e. population stratification) can result in bias and incorrect inferences in genotype-disease association studies, but the effect of population stratification in gene-gene or gene-environment interaction studies has not been addressed. We used logistic regression models to fit multiplicative interactions between two dichotomous variables that represented genetic and/or environmental factors for a binary disease outcome in a hypothetical cohort of multiple ethnicities. Biases in main effects and interactions due to population stratification were evaluated by comparing regression coefficients in mis-specified models that ignored ethnicities with their counterparts in models that accounted for ethnicities. We showed that biases in main effects and interactions were constrained by the differences in disease risks across the ethnicities. Therefore, large biases due to population stratification are not possible when baseline disease risk differences among ethnicities are small or moderate. Numerical examples of biases in genotype-genotype and/or genotype-environment interactions suggested that biases due to population stratification for main effects were generally small but could become large for studies of interactions, particularly when strong linkage disequilibrium between genes or large correlations between genetic and environmental factors existed. However, when linkage disequilibrium among genes or correlations among genes and environments were small, biases to main effects or interaction odds ratios were small to nonexistent. (Cancer Epidemiol Biomarkers Prev 2006;15(1):124–32)
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