Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model

Detalhes bibliográficos
Autor(a) principal: Bueno Filho, Julio Sílvio de Sousa
Data de Publicação: 2011
Outros Autores: Morota, Gota, Tran, Quoc, Maenner, Matthew J, Vera-Cala, Lina M, Engelman, Corinne D, Meyers, Kristin J
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/38602
Resumo: Next-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.
id UFLA_53f3afc6a4ee5b991ceceb2d6f5ec1d6
oai_identifier_str oai:localhost:1/38602
network_acronym_str UFLA
network_name_str Repositório Institucional da UFLA
repository_id_str
spelling Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture modelSequencing technologiesGenetic associationBayesian hierarchical mixture modelMetropolis Hasting algorithmGibbs samplingNext-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.National Center for Biotechnology Information, U.S. National Library of Medicine2020-01-23T13:56:53Z2020-01-23T13:56:53Z2011-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfBUENO 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.http://repositorio.ufla.br/jspui/handle/1/38602BMC Proceedingsreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessBueno Filho, Julio Sílvio de SousaMorota, GotaTran, QuocMaenner, Matthew JVera-Cala, Lina MEngelman, Corinne DMeyers, Kristin Jeng2023-05-26T19:36:33Zoai:localhost:1/38602Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-26T19:36:33Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model
title Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model
spellingShingle Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model
Bueno Filho, Julio Sílvio de Sousa
Sequencing technologies
Genetic association
Bayesian hierarchical mixture model
Metropolis Hasting algorithm
Gibbs sampling
title_short Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model
title_full Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model
title_fullStr Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model
title_full_unstemmed Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model
title_sort Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model
author Bueno Filho, Julio Sílvio de Sousa
author_facet Bueno Filho, Julio Sílvio de Sousa
Morota, Gota
Tran, Quoc
Maenner, Matthew J
Vera-Cala, Lina M
Engelman, Corinne D
Meyers, Kristin J
author_role author
author2 Morota, Gota
Tran, Quoc
Maenner, Matthew J
Vera-Cala, Lina M
Engelman, Corinne D
Meyers, Kristin J
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Bueno Filho, Julio Sílvio de Sousa
Morota, Gota
Tran, Quoc
Maenner, Matthew J
Vera-Cala, Lina M
Engelman, Corinne D
Meyers, Kristin J
dc.subject.por.fl_str_mv Sequencing technologies
Genetic association
Bayesian hierarchical mixture model
Metropolis Hasting algorithm
Gibbs sampling
topic Sequencing technologies
Genetic association
Bayesian hierarchical mixture model
Metropolis Hasting algorithm
Gibbs sampling
description Next-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.
publishDate 2011
dc.date.none.fl_str_mv 2011-11
2020-01-23T13:56:53Z
2020-01-23T13:56:53Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv BUENO 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.
http://repositorio.ufla.br/jspui/handle/1/38602
identifier_str_mv BUENO 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.
url http://repositorio.ufla.br/jspui/handle/1/38602
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv National Center for Biotechnology Information, U.S. National Library of Medicine
publisher.none.fl_str_mv National Center for Biotechnology Information, U.S. National Library of Medicine
dc.source.none.fl_str_mv BMC Proceedings
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
_version_ 1815439162296238080