Genotype-based matching to correct for population stratification in large-scale case-control genetic association studies
dc.contributor.author | Guan, Weihua | en_US |
dc.contributor.author | Liang, Liming | en_US |
dc.contributor.author | Boehnke, Michael | en_US |
dc.contributor.author | Abecasis, Gonçalo R. | en_US |
dc.date.accessioned | 2009-09-02T14:38:18Z | |
dc.date.available | 2010-10-05T18:27:29Z | en_US |
dc.date.issued | 2009-09 | en_US |
dc.identifier.citation | Guan, Weihua; Liang, Liming; Boehnke, Michael; Abecasis, GonÇalo R. (2009). "Genotype-based matching to correct for population stratification in large-scale case-control genetic association studies." Genetic Epidemiology 33(6): 508-517. <http://hdl.handle.net/2027.42/63598> | en_US |
dc.identifier.issn | 0741-0395 | en_US |
dc.identifier.issn | 1098-2272 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/63598 | |
dc.description.abstract | 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. © 2009 Wiley-Liss, Inc. | en_US |
dc.format.extent | 322577 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Wiley Subscription Services, Inc., A Wiley Company | en_US |
dc.subject.other | Life and Medical Sciences | en_US |
dc.subject.other | Genetics | en_US |
dc.title | Genotype-based matching to correct for population stratification in large-scale case-control genetic association studies | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Biological Chemistry | en_US |
dc.subject.hlbsecondlevel | Genetics | en_US |
dc.subject.hlbsecondlevel | Molecular, Cellular and Developmental Biology | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan ; These authors contributed equally to this work. | en_US |
dc.contributor.affiliationum | Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationum | Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationum | Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan ; Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor MI 48109-2029 | en_US |
dc.identifier.pmid | 19170134 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/63598/1/20403_ftp.pdf | |
dc.identifier.doi | 10.1002/gepi.20403 | en_US |
dc.identifier.source | Genetic Epidemiology | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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