Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , |
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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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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1816829605119524864 |