Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162023000100502 |
Resumo: | ABSTRACT Among the multi-trait models selected to study several traits and environments jointly, the Bayesian framework has been a preferred tool when constructing a more complex and biologically realistic model. In most cases, non-informative prior distributions are adopted in studies using the Bayesian approach. However, the Bayesian approach presents more accurate estimates when informative prior distributions are used. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models within a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data on rice. The study involved data pertaining to rice (Oryza sativa L.) genotypes in three environments and five crop seasons (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components, genetic and non-genetic parameters were estimated using the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of individual narrow-sense heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. More informative prior distributions make it possible to detect genetic correlations between traits, which cannot be achieved with non-informative prior distributions. Therefore, this mechanism presented to update knowledge for an elicitation of an informative prior distribution can be efficiently applied in rice breeding programs. |
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Scientia Agrícola (Online) |
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Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in riceMCMCgenetic correlationgenetic improvementheritabilityprior distributionABSTRACT Among the multi-trait models selected to study several traits and environments jointly, the Bayesian framework has been a preferred tool when constructing a more complex and biologically realistic model. In most cases, non-informative prior distributions are adopted in studies using the Bayesian approach. However, the Bayesian approach presents more accurate estimates when informative prior distributions are used. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models within a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data on rice. The study involved data pertaining to rice (Oryza sativa L.) genotypes in three environments and five crop seasons (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components, genetic and non-genetic parameters were estimated using the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of individual narrow-sense heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. More informative prior distributions make it possible to detect genetic correlations between traits, which cannot be achieved with non-informative prior distributions. Therefore, this mechanism presented to update knowledge for an elicitation of an informative prior distribution can be efficiently applied in rice breeding programs.Escola Superior de Agricultura "Luiz de Queiroz"2023-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162023000100502Scientia Agricola v.80 2023reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/1678-992x-2022-0056info:eu-repo/semantics/openAccessAzevedo,Camila FerreiraBarreto,Cynthia Aparecida ValiatiSuela,Matheus MassariolNascimento,MoysésSilva Júnior,Antônio Carlos daNascimento,Ana Carolina CampanaCruz,Cosme DamiãoSoraes,Plínio Césareng2022-10-06T00:00:00Zoai:scielo:S0103-90162023000100502Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2022-10-06T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice |
title |
Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice |
spellingShingle |
Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice Azevedo,Camila Ferreira MCMC genetic correlation genetic improvement heritability prior distribution |
title_short |
Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice |
title_full |
Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice |
title_fullStr |
Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice |
title_full_unstemmed |
Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice |
title_sort |
Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice |
author |
Azevedo,Camila Ferreira |
author_facet |
Azevedo,Camila Ferreira Barreto,Cynthia Aparecida Valiati Suela,Matheus Massariol Nascimento,Moysés Silva Júnior,Antônio Carlos da Nascimento,Ana Carolina Campana Cruz,Cosme Damião Soraes,Plínio César |
author_role |
author |
author2 |
Barreto,Cynthia Aparecida Valiati Suela,Matheus Massariol Nascimento,Moysés Silva Júnior,Antônio Carlos da Nascimento,Ana Carolina Campana Cruz,Cosme Damião Soraes,Plínio César |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Azevedo,Camila Ferreira Barreto,Cynthia Aparecida Valiati Suela,Matheus Massariol Nascimento,Moysés Silva Júnior,Antônio Carlos da Nascimento,Ana Carolina Campana Cruz,Cosme Damião Soraes,Plínio César |
dc.subject.por.fl_str_mv |
MCMC genetic correlation genetic improvement heritability prior distribution |
topic |
MCMC genetic correlation genetic improvement heritability prior distribution |
description |
ABSTRACT Among the multi-trait models selected to study several traits and environments jointly, the Bayesian framework has been a preferred tool when constructing a more complex and biologically realistic model. In most cases, non-informative prior distributions are adopted in studies using the Bayesian approach. However, the Bayesian approach presents more accurate estimates when informative prior distributions are used. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models within a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data on rice. The study involved data pertaining to rice (Oryza sativa L.) genotypes in three environments and five crop seasons (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components, genetic and non-genetic parameters were estimated using the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of individual narrow-sense heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. More informative prior distributions make it possible to detect genetic correlations between traits, which cannot be achieved with non-informative prior distributions. Therefore, this mechanism presented to update knowledge for an elicitation of an informative prior distribution can be efficiently applied in rice breeding programs. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162023000100502 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162023000100502 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-992x-2022-0056 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.80 2023 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1748936466152554496 |