Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data.

Detalhes bibliográficos
Autor(a) principal: LIMA, L. P.
Data de Publicação: 2022
Outros Autores: AZEVEDO, C. F., RESENDE, M. D. V. de, NASCIMENTO, M., SILVA, F. F. e
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139182
Resumo: The development of efficient methods for genome-wide association studies (GWAS) between quantitative trait loci (QTL) and genetic values is extremely important to animal and plant breeding programs. Bayesian approaches that aim to select regions of single nucleotide polymorphisms (SNPs) proved to be efficient, indicating genes with important effects. Among the selection criteria for SNPs or regions, selection criterion by percentage of variance can be explained by genomic regions (%var), selection of tag SNPs, and selection based on the window posterior probability of association (WPPA). To also detect potentially associated regions, we proposed measuring posterior probability of the interval PPint), which aims to select regions based on the markers of greatest effects. Therefore, the objective of this work was to evaluate these approaches, in terms of efficiency in selecting and identifying markers or regions located within or close to genes associated with traits. This study also aimed to compare these methodologies with single-marker analyses. To accomplish this, simulated data were used in six scenarios, with SNPs allocated in non?overlapping genomic regions. Considering traits with oligogenic inheritance, WPPA criterion followed by %var and PPint criteria were shown to be superior, presenting higher values of detection power, capturing higher percentages of genetic variance and larger areas. For traits with polygenic inheritance, PPint and WPPA criteria were considered superior. Single?marker analyses identified SNPs associated only in oligogenic inheritance scenarios and was lower than the other criteria.
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spelling Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data.Método de MelhoramentoMelhoramento Genético VegetalGenomicsMolecular modelsGenetic variancePlant breedingThe development of efficient methods for genome-wide association studies (GWAS) between quantitative trait loci (QTL) and genetic values is extremely important to animal and plant breeding programs. Bayesian approaches that aim to select regions of single nucleotide polymorphisms (SNPs) proved to be efficient, indicating genes with important effects. Among the selection criteria for SNPs or regions, selection criterion by percentage of variance can be explained by genomic regions (%var), selection of tag SNPs, and selection based on the window posterior probability of association (WPPA). To also detect potentially associated regions, we proposed measuring posterior probability of the interval PPint), which aims to select regions based on the markers of greatest effects. Therefore, the objective of this work was to evaluate these approaches, in terms of efficiency in selecting and identifying markers or regions located within or close to genes associated with traits. This study also aimed to compare these methodologies with single-marker analyses. To accomplish this, simulated data were used in six scenarios, with SNPs allocated in non?overlapping genomic regions. Considering traits with oligogenic inheritance, WPPA criterion followed by %var and PPint criteria were shown to be superior, presenting higher values of detection power, capturing higher percentages of genetic variance and larger areas. For traits with polygenic inheritance, PPint and WPPA criteria were considered superior. Single?marker analyses identified SNPs associated only in oligogenic inheritance scenarios and was lower than the other criteria.LEÍSA PIRES LIMA, UFV; CAMILA FERREIRA AZEVEDO, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa; MOYSÉS NASCIMENTO, UFV; FABYANO FONSECA E SILVA, UFV.LIMA, L. P.AZEVEDO, C. F.RESENDE, M. D. V. deNASCIMENTO, M.SILVA, F. F. e2022-01-19T15:00:30Z2022-01-19T15:00:30Z2022-01-192022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleScientia Agricola, v. 79, n. 3, p. 1-10, 2022.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139182enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2022-01-19T15:00:40Zoai:www.alice.cnptia.embrapa.br:doc/1139182Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542022-01-19T15:00:40falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542022-01-19T15:00:40Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data.
title Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data.
spellingShingle Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data.
LIMA, L. P.
Método de Melhoramento
Melhoramento Genético Vegetal
Genomics
Molecular models
Genetic variance
Plant breeding
title_short Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data.
title_full Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data.
title_fullStr Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data.
title_full_unstemmed Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data.
title_sort Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data.
author LIMA, L. P.
author_facet LIMA, L. P.
AZEVEDO, C. F.
RESENDE, M. D. V. de
NASCIMENTO, M.
SILVA, F. F. e
author_role author
author2 AZEVEDO, C. F.
RESENDE, M. D. V. de
NASCIMENTO, M.
SILVA, F. F. e
author2_role author
author
author
author
dc.contributor.none.fl_str_mv LEÍSA PIRES LIMA, UFV; CAMILA FERREIRA AZEVEDO, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa; MOYSÉS NASCIMENTO, UFV; FABYANO FONSECA E SILVA, UFV.
dc.contributor.author.fl_str_mv LIMA, L. P.
AZEVEDO, C. F.
RESENDE, M. D. V. de
NASCIMENTO, M.
SILVA, F. F. e
dc.subject.por.fl_str_mv Método de Melhoramento
Melhoramento Genético Vegetal
Genomics
Molecular models
Genetic variance
Plant breeding
topic Método de Melhoramento
Melhoramento Genético Vegetal
Genomics
Molecular models
Genetic variance
Plant breeding
description The development of efficient methods for genome-wide association studies (GWAS) between quantitative trait loci (QTL) and genetic values is extremely important to animal and plant breeding programs. Bayesian approaches that aim to select regions of single nucleotide polymorphisms (SNPs) proved to be efficient, indicating genes with important effects. Among the selection criteria for SNPs or regions, selection criterion by percentage of variance can be explained by genomic regions (%var), selection of tag SNPs, and selection based on the window posterior probability of association (WPPA). To also detect potentially associated regions, we proposed measuring posterior probability of the interval PPint), which aims to select regions based on the markers of greatest effects. Therefore, the objective of this work was to evaluate these approaches, in terms of efficiency in selecting and identifying markers or regions located within or close to genes associated with traits. This study also aimed to compare these methodologies with single-marker analyses. To accomplish this, simulated data were used in six scenarios, with SNPs allocated in non?overlapping genomic regions. Considering traits with oligogenic inheritance, WPPA criterion followed by %var and PPint criteria were shown to be superior, presenting higher values of detection power, capturing higher percentages of genetic variance and larger areas. For traits with polygenic inheritance, PPint and WPPA criteria were considered superior. Single?marker analyses identified SNPs associated only in oligogenic inheritance scenarios and was lower than the other criteria.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-19T15:00:30Z
2022-01-19T15:00:30Z
2022-01-19
2022
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Scientia Agricola, v. 79, n. 3, p. 1-10, 2022.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139182
identifier_str_mv Scientia Agricola, v. 79, n. 3, p. 1-10, 2022.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139182
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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