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Author Notes:

Correspondence: Michael P. Epstein, Ph.D., Department of Human Genetics, Emory University School of Medicine 615 Michael Street, Suite 301, Atlanta, GA 30322; Phone: (404)712-8289, Fax: (404)727-3949, Email: mpepste@emory.edu

Acknowledgments: We thank Drs. Michael Boehnke, Weihua Guan, and Liming Liang as well as two anonymous reviewers for helpful comments on a previous version of this article.

The dataset used for the analyses described in this manuscript were obtained from the database of Genotype and Phenotype (dbGaP) found at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000021.v2.p1.

Samples and associated phenotype data for ‘Genome-Wide Association Study of Schizophrenia’ were provided by Pablo V. Gejman, MD.


Research Funding:

This work was supported by National Institutes of Health grants HG003618 (to M.P.E and R.D.) and HL077663 (to A.S.A).

Funding support for ‘Genome-Wide Association Study of Schizophrenia’ was provided by National Institutes of Health and the genotyping of samples was provided through the Genetic Association Information Network (GAIN).

Stratification Score Matching Improves Correction for Confounding by Population Stratification in Case-Control Association Studies


Journal Title:

Genetic Epidemiology


Volume 36, Number 3


, Pages 195-205

Type of Work:

Article | Post-print: After Peer Review


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 (GWAS) 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 non-confounding 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., 2007, AJHG: 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 that 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.

Copyright information:

© 2012 Wiley Periodicals, Inc.

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