Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , , , |
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. |
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Repositório Institucional da UFLA |
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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 |