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Contrasting multi-site genotypic distributions among discordant quantitative phenotypes: the APOA1/C3/A4/A5 gene cluster and cardiovascular disease risk factors

dc.contributor.authorPayseur, Bret A.en_US
dc.contributor.authorClark, Andrew G.en_US
dc.contributor.authorHixson, James E.en_US
dc.contributor.authorBoerwinkle, Ericen_US
dc.contributor.authorSing, Charles F.en_US
dc.date.accessioned2007-09-18T19:20:36Z
dc.date.available2007-09-18T19:20:36Z
dc.date.issued2006-09en_US
dc.identifier.citationPayseur, Bret A.; Clark, Andrew G.; Hixson, James; Boerwinkle, Eric; Sing, Charles F. (2006). "Contrasting multi-site genotypic distributions among discordant quantitative phenotypes: the APOA1/C3/A4/A5 gene cluster and cardiovascular disease risk factors." Genetic Epidemiology 30(6): 508-518. <http://hdl.handle.net/2027.42/55790>en_US
dc.identifier.issn0741-0395en_US
dc.identifier.issn1098-2272en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/55790
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=16800005&dopt=citationen_US
dc.description.abstractMost tests of association between DNA sequence variation and quantitative phenotypes in samples of randomly chosen individuals rely on specification of genotypic strata followed by comparison of phenotypes across these strata. This strategy often succeeds when phenotypic differences are caused by one or two single nucleotide polymorphisms (SNPs) among the surveyed markers. However, when multiple-SNP haplotypes account for observed phenotypic variation, identification of the best partitioning requires examination of an inordinate number of SNP combinations. An alternative approach is to rank individuals by their phenotypic measures and ask whether attributes of the genotypic variation show a non-random distribution along this phenotypic ranking. One simple version of this strategy selects the top and bottom tails of the distribution, and then tests whether genotypes from these two samples are drawn from a single population. This framework does not require the recovery of phased haplotypes and allows contrasts between large numbers of sites at once. We use a method based on this approach to identify associations between plasma triglyceride level, a risk factor for cardiovascular disease, and multi-site genotypes located in the APOA1/C3/A4/A5 cluster of apolipoprotein genes in unrelated individuals (1,071 African-American females, 780 African-American males, 1,036 European-American females, and 930 European-American males) sampled from four US cities as part of the Coronary Artery Risk Development in Young Adults (CARDIA) study. Method performance is investigated using simulations that model genealogical variation and different genetic architectures. Results indicate that this multi-site test can identify genotype-phenotype associations with reasonable power, including those generated by some simple epistatic models. Genet. Epidemiol . 2006. © 2006 Wiley-Liss, Inc.en_US
dc.format.extent247880 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherWiley Subscription Services, Inc., A Wiley Companyen_US
dc.subject.otherLife and Medical Sciencesen_US
dc.subject.otherGeneticsen_US
dc.titleContrasting multi-site genotypic distributions among discordant quantitative phenotypes: the APOA1/C3/A4/A5 gene cluster and cardiovascular disease risk factorsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelBiological Chemistryen_US
dc.subject.hlbsecondlevelGeneticsen_US
dc.subject.hlbsecondlevelMolecular, Cellular and Developmental Biologyen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Human Genetics, University of Michigan, Ann Arbor, Michiganen_US
dc.contributor.affiliationotherDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, New York ; Laboratory of Genetics, Genetics/Biotechnology 2428, University of Wisconsin, Madison, WI 53706en_US
dc.contributor.affiliationotherDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, New Yorken_US
dc.contributor.affiliationotherHuman Genetics Center, University of Texas Health Sciences Center, Houston, Texasen_US
dc.contributor.affiliationotherHuman Genetics Center, University of Texas Health Sciences Center, Houston, Texasen_US
dc.identifier.pmid16800005en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/55790/1/20163_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/gepi.20163en_US
dc.identifier.sourceGenetic Epidemiologyen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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