alexa Matching on Race and Ethnicity in Case-Control Studies
ISSN: 2161-1165

Epidemiology: Open Access
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Research Article

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

Anand P. Chokkalingam1*, Melinda C. Aldrich2, Karen Bartley1, Ling-I Hsu1, Catherine Metayer1, Lisa F. Barcellos1, Joseph L. Wiemels3, John K. Wiencke4, Patricia A. Buffler1 and Steve Selvin1

1School of Public Health, University of California Berkeley, Berkeley,USA

2Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee

3Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California,USA

4Department of Neurological Surgery, University of California, San Francisco San Francisco, California,USA

*Corresponding Author:
Dr. Anand P. Chokkalingam
Division of Epidemiology
UC Berkeley School of Public Health
1995 University Ave, Ste 460, Berkeley CA 94704,USA
Tel: (510) 642-8375
Fax: (510) 643-1735
E-mail: [email protected]

Received date: August 24, 2011; Accepted date: September 20, 2011; Published date: September 29, 2011

Citation: Chokkalingam AP, Aldrich MC, Bartley K, Hsu LI, Metayer C, et al. (2011) Matching on Race and Ethnicity in Case-Control Studies as a Means of Control for Population Stratification. Epidemiol 1:101. doi:10.4172/2161-1165.1000101

Copyright: © 2011 Chokkalingam AP, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

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

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