Meta-analysis of genetic association studies and adjustment for multiple testing of correlated SNPs and traits
dc.contributor.author | Conneely, Karen N. | en_US |
dc.contributor.author | Boehnke, Michael | en_US |
dc.date.accessioned | 2010-11-03T15:19:33Z | |
dc.date.available | 2011-03-01T16:26:46Z | en_US |
dc.date.issued | 2010-11 | en_US |
dc.identifier.citation | Conneely, Karen N.; Boehnke, Michael (2010). "Meta-analysis of genetic association studies and adjustment for multiple testing of correlated SNPs and traits." Genetic Epidemiology 34(7): 739-746. <http://hdl.handle.net/2027.42/78213> | en_US |
dc.identifier.issn | 0741-0395 | en_US |
dc.identifier.issn | 1098-2272 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/78213 | |
dc.description.abstract | Meta-analysis has become a key component of well-designed genetic association studies due to the boost in statistical power achieved by combining results across multiple samples of individuals and the need to validate observed associations in independent studies. Meta-analyses of genetic association studies based on multiple SNPs and traits are subject to the same multiple testing issues as single-sample studies, but it is often difficult to adjust accurately for the multiple tests. Procedures such as Bonferroni may control the type-I error rate but will generally provide an overly harsh correction if SNPs or traits are correlated. Depending on study design, availability of individual-level data, and computational requirements, permutation testing may not be feasible in a meta-analysis framework. In this article, we present methods for adjusting for multiple correlated tests under several study designs commonly employed in meta-analyses of genetic association tests. Our methods are applicable to both prospective meta-analyses in which several samples of individuals are analyzed with the intent to combine results, and retrospective meta-analyses, in which results from published studies are combined, including situations in which (1) individual-level data are unavailable, and (2) different sets of SNPs are genotyped in different studies due to random missingness or two-stage design. We show through simulation that our methods accurately control the rate of type I error and achieve improved power over multiple testing adjustments that do not account for correlation between SNPs or traits. Genet. Epidemiol . 34: 739-746, 2010. © 2010 Wiley-Liss, Inc. | en_US |
dc.format.extent | 191458 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 | Meta-analysis of genetic association studies and adjustment for multiple testing of correlated SNPs and traits | 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, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationother | Department of Human Genetics, Emory University, Atlanta, Georgia ; Department of Human Genetics, Emory University, 615 Michael Street Suite 301, Atlanta, GA 30322 | en_US |
dc.identifier.pmid | 20878715 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/78213/1/20538_ftp.pdf | |
dc.identifier.doi | 10.1002/gepi.20538 | en_US |
dc.identifier.source | Genetic Epidemiology | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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