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dc.creatorBueno Filho, Julio Sílvio de Sousa-
dc.creatorMorota, Gota-
dc.creatorTran, Quoc-
dc.creatorMaenner, Matthew J-
dc.creatorVera-Cala, Lina M-
dc.creatorEngelman, Corinne D-
dc.creatorMeyers, Kristin J-
dc.date.accessioned2020-01-23T13:56:53Z-
dc.date.available2020-01-23T13:56:53Z-
dc.date.issued2011-11-
dc.identifier.citationBUENO FILHO, J. S. de S. et al. Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model. BMC Proceedings, Bethesda MD, v. 5, Nov. 2011. Suplemento 9.pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/38602-
dc.description.abstractNext-generation sequencing technologies are rapidly changing the field of genetic epidemiology and enabling exploration of the full allele frequency spectrum underlying complex diseases. Although sequencing technologies have shifted our focus toward rare genetic variants, statistical methods traditionally used in genetic association studies are inadequate for estimating effects of low minor allele frequency variants. Four our study we use the Genetic Analysis Workshop 17 data from 697 unrelated individuals (genotypes for 24,487 autosomal variants from 3,205 genes). We apply a Bayesian hierarchical mixture model to identify genes associated with a simulated binary phenotype using a transformed genotype design matrix weighted by allele frequencies. A Metropolis Hasting algorithm is used to jointly sample each indicator variable and additive genetic effect pair from its conditional posterior distribution, and remaining parameters are sampled by Gibbs sampling. This method identified 58 genes with a posterior probability greater than 0.8 for being associated with the phenotype. One of these 58 genes, PIK3C2B was correctly identified as being associated with affected status based on the simulation process. This project demonstrates the utility of Bayesian hierarchical mixture models using a transformed genotype matrix to detect genes containing rare and common variants associated with a binary phenotype.pt_BR
dc.languageen_USpt_BR
dc.publisherNational Center for Biotechnology Information, U.S. National Library of Medicinept_BR
dc.rightsacesso abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceBMC Proceedingspt_BR
dc.subjectSequencing technologiespt_BR
dc.subjectGenetic associationpt_BR
dc.subjectBayesian hierarchical mixture modelpt_BR
dc.subjectMetropolis Hasting algorithmpt_BR
dc.subjectGibbs samplingpt_BR
dc.titleAnalysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture modelpt_BR
dc.typeArtigopt_BR
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