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Genetic Epidemiology 33: 508517 (2009) Genotype-Based Matching to Correct for Population Stratification
 

Summary: Genetic Epidemiology 33: 508≠517 (2009)
Genotype-Based Matching to Correct for Population Stratification
in Large-Scale Case-Control Genetic Association Studies
Weihua Guan√√, Liming Liang√√, Michael Boehnke, and Gonc-alo R. Abecasis√
Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan
Genome-wide association studies are helping to dissect the etiology of complex diseases. Although case-control association
tests are generally more powerful than family-based association tests, population stratification can lead to spurious disease-
marker association or mask a true association. Several methods have been proposed to match cases and controls prior to
genotyping, using family information or epidemiological data, or using genotype data for a modest number of genetic
markers. Here, we describe a genetic similarity score matching (GSM) method for efficient matched analysis of cases and
controls in a genome-wide or large-scale candidate gene association study. GSM comprises three steps: (1) calculating
similarity scores for pairs of individuals using the genotype data; (2) matching sets of cases and controls based on the
similarity scores so that matched cases and controls have similar genetic background; and (3) using conditional logistic
regression to perform association tests. Through computer simulation we show that GSM correctly controls false-positive
rates and improves power to detect true disease predisposing variants. We compare GSM to genomic control using
computer simulations, and find improved power using GSM. We suggest that initial matching of cases and controls prior to
genotyping combined with careful re-matching after genotyping is a method of choice for genome-wide association studies.
Genet. Epidemiol. 33:508≠517, 2009. r 2009 Wiley-Liss, Inc.
Key words: population stratification; genome-wide association; genetic similarity
Contract grant sponsor: National Institutes of Health; Contract grant numbers: HG000376; HG02651; HL084729.

  

Source: Abecasis, Goncalo - Department of Biostatistics, University of Michigan
Liang, Liming - Departments of Biostatistics & Epidemiology, Harvard University

 

Collections: Biology and Medicine; Mathematics