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Stratification-Score Matching Improves Correction for Confounding by Population Stratification in Case-Control Association Studies
Article first published online: 19 APR 2012
© 2012 Wiley Periodicals, Inc.
Volume 36, Issue 3, pages 195–205, April 2012
How to Cite
Epstein, M. P., Duncan, R., Broadaway, K. A., He, M., Allen, A. S. and Satten, G. A. (2012), Stratification-Score Matching Improves Correction for Confounding by Population Stratification in Case-Control Association Studies. Genet. Epidemiol., 36: 195–205. doi: 10.1002/gepi.21611
Additional Supporting Information may be found in the online version of this article.
Contract grant sponsor: National Institutes of Health; Contract grant numbers: HG003618; HL077663.
- Issue published online: 19 APR 2012
- Article first published online: 19 APR 2012
- Manuscript Accepted: 6 DEC 2011
- Manuscript Received: 24 OCT 2011
- National Institutes of Health. Grant Numbers: HG003618, HL077663
- population stratification;
- case-control study;
Proper control of confounding due to population stratification is crucial for valid analysis of case-control association studies. Fine matching of cases and controls based on genetic ancestry is an increasingly popular strategy to correct for such confounding, both in genome-wide association studies (GWASs) as well as studies that employ next-generation sequencing, where matching can be used when selecting a subset of participants from a GWAS for rare-variant analysis. Existing matching methods match on measures of genetic ancestry that combine multiple components of ancestry into a scalar quantity. However, we show that including nonconfounding ancestry components in a matching criterion can lead to inaccurate matches, and hence to an improper control of confounding. To resolve this issue, we propose a novel method that assigns cases and controls to matched strata based on the stratification score (Epstein et al.  Am J Hum Genet 80:921–930), which is the probability of disease given genomic variables. Matching on the stratification score leads to more accurate matches because case participants are matched to control participants who have a similar risk of disease given ancestry information. We illustrate our matching method using the African-American arm of the GAIN GWAS of schizophrenia. In this study, we observe that confounding due to stratification can be resolved by our matching approach but not by other existing matching procedures. We also use simulated data to show our novel matching approach can provide a more appropriate correction for population stratification than existing matching approaches.