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dc.creatorFoulkes, AS
dc.creatorYucel, R
dc.creatorLi, X
dc.date.accessioned2021-01-28T20:51:32Z
dc.date.available2021-01-28T20:51:32Z
dc.date.issued2008-10-01
dc.identifier.issn1465-4644
dc.identifier.issn1468-4357
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5078
dc.identifier.other18343883 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5096
dc.description.abstractThis manuscript describes a novel, linear mixed-effects model-fitting technique for the setting in which correlated data indicators are not completely observed. Mixed modeling is a useful analytical tool for characterizing genotype-phenotype associations among multiple potentially informative genetic loci. This approach involves grouping individuals into genetic clusters, where individuals in the same cluster have similar or identical multilocus genotypes. In haplotype-based investigations of unrelated individuals, corresponding cluster assignments are unobservable since the alignment of alleles within chromosomal copies is not generally observed. We derive an expectation conditional maximization approach to estimation in the mixed modeling setting, where cluster assignments are ambiguous. The approach has broad relevance to the analysis of data with missing correlated data identifiers. An example is provided based on data arising from a cohort of human immunodeficiency virus type-1-infected individuals at risk for antiretroviral therapy-associated dyslipidemia. © 2008 The Authors.
dc.format.extent635-657
dc.language.isoen
dc.relation.haspartBiostatistics
dc.relation.isreferencedbyOxford University Press (OUP)
dc.rightsCC BY-NC
dc.rights.urihttp://creativecommons.org/licenses/by-nc/2.0/uk/
dc.subjectexpectation conditional maximization
dc.subjectgenotype
dc.subjecthaplotype
dc.subjectHIV-1
dc.subjectlipids
dc.subjectmissing identifiers
dc.subjectmixed-effects models
dc.subjectphenotype
dc.subjectpopulation-based genetic association studies
dc.titleA likelihood-based approach to mixed modeling with ambiguity in cluster identifiers
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1093/biostatistics/kxm055
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-01-28T20:51:29Z
refterms.dateFOA2021-01-28T20:51:33Z


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