Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information

Detalhes bibliográficos
Autor(a) principal: Nermy Ribeiro Valadares
Data de Publicação: 2023
Outros Autores: Ana Clara Gonçalves Fernandes, Clovis Henrique Oliveira Rodrigues, Lis Lorena Melúcio Guedes, Jailson Ramos Magalhães, Rayane Aguiar Alves, Valter Carvalho de Andrade Júnior, Alcinei Mistico Azevedo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: https://doi.org/10.4025/actasciagron.v45i1.56160
http://hdl.handle.net/1843/76471
Resumo: The selection of superior sweet potato genotypes using Bayesian inference is an important strategy for genetic improvement. Sweet potatoes are of social and economic importance, being the material for ethanol production. The estimation of variance components and genetic parameters using Bayesian inference is more accurate than that using the frequently used statistical methodologies. This is because the former allows for using a priori knowledge from previous research. Therefore, the present study estimated genetic parameters and selection gains, predicted genetic values, and selected sweet potato genotypes using a Bayesian approach with a priori information. Root shape, soil insect resistance, and root and shoot productivity of 24 sweet potato genotypes were measured. Heritability, genotypic variation coefficient, residual variation coefficient, relative variation index, and selection gains direct, indirect and simultaneous were estimated, and the data were analyzed using Bayesian inference. Data from 11 experiments were used to obtain a priori information. Bayesian inference was a useful tool for decision-making, and significant genetic gains could be achieved with the selection of the evaluated genotypes. Root shape, soil insect resistance, commercial root productivity, and total root productivity showed higher heritability values. Clones UFVJM06, UFVJM40, UFVJM54, UFVJM09, and CAMBRAIA can be used as parents in future breeding programs.
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spelling Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori informationBatata-doceMelhoramento genéticoTeoria bayesiana de decisão estatisticaBiometriaAgricultura -- ExperimentaçãoBatata-doceMelhoramento genéticoTeoria bayesiana de decisão estatisticaBiometriaAgricultura -- ExperimentaçãoThe selection of superior sweet potato genotypes using Bayesian inference is an important strategy for genetic improvement. Sweet potatoes are of social and economic importance, being the material for ethanol production. The estimation of variance components and genetic parameters using Bayesian inference is more accurate than that using the frequently used statistical methodologies. This is because the former allows for using a priori knowledge from previous research. Therefore, the present study estimated genetic parameters and selection gains, predicted genetic values, and selected sweet potato genotypes using a Bayesian approach with a priori information. Root shape, soil insect resistance, and root and shoot productivity of 24 sweet potato genotypes were measured. Heritability, genotypic variation coefficient, residual variation coefficient, relative variation index, and selection gains direct, indirect and simultaneous were estimated, and the data were analyzed using Bayesian inference. Data from 11 experiments were used to obtain a priori information. Bayesian inference was a useful tool for decision-making, and significant genetic gains could be achieved with the selection of the evaluated genotypes. Root shape, soil insect resistance, commercial root productivity, and total root productivity showed higher heritability values. Clones UFVJM06, UFVJM40, UFVJM54, UFVJM09, and CAMBRAIA can be used as parents in future breeding programs.CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas GeraisCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversidade Federal de Minas GeraisBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASUFMG2024-09-16T11:33:03Z2024-09-16T11:33:03Z2023info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.4025/actasciagron.v45i1.561601807-8621http://hdl.handle.net/1843/76471engActa ScientiarumNermy Ribeiro ValadaresAna Clara Gonçalves FernandesClovis Henrique Oliveira RodriguesLis Lorena Melúcio GuedesJailson Ramos MagalhãesRayane Aguiar AlvesValter Carvalho de Andrade JúniorAlcinei Mistico Azevedoinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMG2024-09-16T16:46:53Zoai:repositorio.ufmg.br:1843/76471Repositório InstitucionalPUBhttps://repositorio.ufmg.br/oairepositorio@ufmg.bropendoar:2024-09-16T16:46:53Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.none.fl_str_mv Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information
title Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information
spellingShingle Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information
Nermy Ribeiro Valadares
Batata-doce
Melhoramento genético
Teoria bayesiana de decisão estatistica
Biometria
Agricultura -- Experimentação
Batata-doce
Melhoramento genético
Teoria bayesiana de decisão estatistica
Biometria
Agricultura -- Experimentação
title_short Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information
title_full Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information
title_fullStr Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information
title_full_unstemmed Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information
title_sort Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information
author Nermy Ribeiro Valadares
author_facet Nermy Ribeiro Valadares
Ana Clara Gonçalves Fernandes
Clovis Henrique Oliveira Rodrigues
Lis Lorena Melúcio Guedes
Jailson Ramos Magalhães
Rayane Aguiar Alves
Valter Carvalho de Andrade Júnior
Alcinei Mistico Azevedo
author_role author
author2 Ana Clara Gonçalves Fernandes
Clovis Henrique Oliveira Rodrigues
Lis Lorena Melúcio Guedes
Jailson Ramos Magalhães
Rayane Aguiar Alves
Valter Carvalho de Andrade Júnior
Alcinei Mistico Azevedo
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Nermy Ribeiro Valadares
Ana Clara Gonçalves Fernandes
Clovis Henrique Oliveira Rodrigues
Lis Lorena Melúcio Guedes
Jailson Ramos Magalhães
Rayane Aguiar Alves
Valter Carvalho de Andrade Júnior
Alcinei Mistico Azevedo
dc.subject.por.fl_str_mv Batata-doce
Melhoramento genético
Teoria bayesiana de decisão estatistica
Biometria
Agricultura -- Experimentação
Batata-doce
Melhoramento genético
Teoria bayesiana de decisão estatistica
Biometria
Agricultura -- Experimentação
topic Batata-doce
Melhoramento genético
Teoria bayesiana de decisão estatistica
Biometria
Agricultura -- Experimentação
Batata-doce
Melhoramento genético
Teoria bayesiana de decisão estatistica
Biometria
Agricultura -- Experimentação
description The selection of superior sweet potato genotypes using Bayesian inference is an important strategy for genetic improvement. Sweet potatoes are of social and economic importance, being the material for ethanol production. The estimation of variance components and genetic parameters using Bayesian inference is more accurate than that using the frequently used statistical methodologies. This is because the former allows for using a priori knowledge from previous research. Therefore, the present study estimated genetic parameters and selection gains, predicted genetic values, and selected sweet potato genotypes using a Bayesian approach with a priori information. Root shape, soil insect resistance, and root and shoot productivity of 24 sweet potato genotypes were measured. Heritability, genotypic variation coefficient, residual variation coefficient, relative variation index, and selection gains direct, indirect and simultaneous were estimated, and the data were analyzed using Bayesian inference. Data from 11 experiments were used to obtain a priori information. Bayesian inference was a useful tool for decision-making, and significant genetic gains could be achieved with the selection of the evaluated genotypes. Root shape, soil insect resistance, commercial root productivity, and total root productivity showed higher heritability values. Clones UFVJM06, UFVJM40, UFVJM54, UFVJM09, and CAMBRAIA can be used as parents in future breeding programs.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024-09-16T11:33:03Z
2024-09-16T11:33:03Z
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 https://doi.org/10.4025/actasciagron.v45i1.56160
1807-8621
http://hdl.handle.net/1843/76471
url https://doi.org/10.4025/actasciagron.v45i1.56160
http://hdl.handle.net/1843/76471
identifier_str_mv 1807-8621
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Acta Scientiarum
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
Brasil
ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
UFMG
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
Brasil
ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
UFMG
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
repository.name.fl_str_mv Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)
repository.mail.fl_str_mv repositorio@ufmg.br
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