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Haplotype association analysis for late onset diseases using nuclear family data

dc.contributor.authorLi, Chunen_US
dc.contributor.authorBoehnke, Michaelen_US
dc.date.accessioned2007-05-02T14:16:50Z
dc.date.available2007-05-02T14:16:50Z
dc.date.issued2006-04en_US
dc.identifier.citationLi, Chun; Boehnke, Michael (2006). "Haplotype association analysis for late onset diseases using nuclear family data." Genetic Epidemiology 30(3): 220-230. <http://hdl.handle.net/2027.42/50656>en_US
dc.identifier.issn0741-0395en_US
dc.identifier.issn1098-2272en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/50656
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=16470533&dopt=citationen_US
dc.description.abstractIn haplotype-based association studies for late onset diseases, one attractive design is to use available unaffected spouses as controls (Valle et al. [1998] Diab. Care 21:949–958). Given cases and spouses only, the standard expectation-maximization (EM) algorithm (Dempster et al. [1977] J. R. Stat. Soc. B 39:1–38) for case-control data can be used to estimate haplotype frequencies. But often we will have offspring for at least some of the spouse pairs, and offspring genotypes provide additional information about the haplotypes of the parents. Existing methods may either ignore the offspring information, or reconstruct haplotypes for the subjects using offspring information and discard data from those whose haplotypes cannot be reconstructed with high confidence. Neither of these approaches is efficient, and the latter approach may also be biased. For case-control data with some subjects forming spouse pairs and offspring genotypes available for some spouse pairs or individuals, we propose a unified, likelihood-based method of haplotype inference. The method makes use of available offspring genotype information to apportion ambiguous haplotypes for the subjects. For subjects without offspring genotype information, haplotypes are apportioned as in the standard EM algorithm for case-control data. Our method enables efficient haplotype frequency estimation using an EM algorithm and supports probabilistic haplotype reconstruction with the probability calculated based on the whole sample. We describe likelihood ratio and permutation tests to test for disease-haplotype association, and describe three test statistics that are potentially useful for detecting such an association. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc.en_US
dc.format.extent215077 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.titleHaplotype association analysis for late onset diseases using nuclear family dataen_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, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michiganen_US
dc.contributor.affiliationotherDepartment of Biostatistics, Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee ; Center for Human Genetics Research, Vanderbilt University Medical Center, 519 Light Hall, Nashville, TN 37232-0700en_US
dc.identifier.pmid16470533en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/50656/1/20139_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/gepi.20139en_US
dc.identifier.sourceGenetic Epidemiologyen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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