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

doi:10.4172/2161-1165.1000101

Abstract

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 minimizing systematic bias due to differences in genetic ancestry between cases and controls.

Highlights

In genetic epidemiology studies, potential confounding by genetic ancestry, known as population stratification, may bias results [1,2,3]
Because genotyping data were not available for every individually matched case-control set, we report the results of an unmatched analysis, using unconditional logistic regression to calculate the confounding risk ratio (CRR) and compare risk estimates adjusted for the matching factors to those further adjusted for estimated genetic ancestry
The CRRs for the 1260 candidate gene SNPs assessing potential confounding by genetic ancestry are shown in (Figure 1)

Summary

Introduction

Potential confounding by genetic ancestry, known as population stratification, may bias results [1,2,3]. Large genome-wide association studies often restrict analyses to subjects who conform to a certain degree of ethnic/genetic homogeneity [4]. 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 [2]. Such approaches are desirable under certain circumstances, such as when a study population is being used to type a limited number of genetic variants as part of a replication study, and costs of high-density genotyping are prohibitive. We examine 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

Methods

Results

Conclusion