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

• Type of Material: Article
• Authors: Michael Epstein, Emory University; Richard Duncan, Emory University; K Alaine Broadaway, Emory University; Min He, Duke University; Andrew S. Allen, Duke University; Glen Alan Satten, Emory University
• Language: English
• Date: 2012-04
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2012 Wiley Periodicals, Inc.
• Final Published Version (URL): http://onlinelibrary.wiley.com/doi/10.1002/gepi.21611/abstract
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 36
• Issue: 3
• Start Page: 195
• End Page: 205
• Grant/Funding Information: 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).; This work was supported by National Institutes of Health grants HG003618 (to M.P.E and R.D.) and HL077663 (to A.S.A).
• Research Categories: Health Sciences, Epidemiology

Abstract

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.