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Estimating the power of variance component linkage analysis in large pedigrees

dc.contributor.authorChen, Wei-Minen_US
dc.contributor.authorAbecasis, Gonçalo R.en_US
dc.date.accessioned2007-09-18T19:20:27Z
dc.date.available2007-09-18T19:20:27Z
dc.date.issued2006-09en_US
dc.identifier.citationChen, Wei-Min; Abecasis, GonÇalo R. (2006). "Estimating the power of variance component linkage analysis in large pedigrees." Genetic Epidemiology 30(6): 471-484. <http://hdl.handle.net/2027.42/55789>en_US
dc.identifier.issn0741-0395en_US
dc.identifier.issn1098-2272en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/55789
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=16685720&dopt=citationen_US
dc.description.abstractVariance component linkage analysis is commonly used to map quantitative trait loci (QTLs) in general pedigrees. Large pedigrees are especially attractive for these studies because they provide greater power per genotyped individual than small pedigrees. We propose accurate and computationally efficient methods to calculate the analytical power of variance component linkage analysis that can accommodate large pedigrees. Our analytical power computation involves the approximation of the noncentrality parameter for the likelihood-ratio test by its Taylor expansions. We develop efficient algorithms to compute the second and third moments of the identical by descent (IBD) sharing distribution and enable rapid computation of the Taylor expansions. Our algorithms take advantage of natural symmetries in pedigrees and can accurately analyze many large pedigrees in a few seconds. We verify the accuracy of our power calculation via simulation in pedigrees with 2–5 generations and 2–8 siblings per sibship. We apply this proposed analytical power calculation to 98 quantitative traits in a cohort study of 6,148 Sardinians in which the largest pedigree includes 625 phenotyped individuals. Simulations based on eight representative traits show that the difference between our analytical estimation of the expected LOD score and the average of simulated LOD scores is less than 0.05 (1.5%). Although our analytical calculations are for a fully informative marker locus, in the settings we examined power was similar to what could be attained with a single nucleotide polymorphism (SNP) mapping panel (with >1 SNP/cM). Our algorithms for power analysis together with polygenic analysis are implemented in a freely available computer program, POLY. Genet. Epidemiol . 2006. © 2006 Wiley-Liss, Inc.en_US
dc.format.extent209655 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.titleEstimating the power of variance component linkage analysis in large pedigreesen_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 Biostatistics, University of Michigan, Ann Arbor, MI ; Center for Statistical Genetics, Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109en_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, MIen_US
dc.identifier.pmid16685720en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/55789/1/20160_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/gepi.20160en_US
dc.identifier.sourceGenetic Epidemiologyen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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