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

Detalhes bibliográficos
Autor(a) principal: Ribeiro Valadares , Nermy
Data de Publicação: 2022
Outros Autores: Fernandes, Ana Clara Gonçalves, Rodrigues , Clóvis Henrique Oliveira, Guedes, Lis Lorena Melúcio, Magalhães, Jailson Ramos, Alves , Rayane Aguiar, Andrade Júnior , Valter Carvalho de, Azevedo, Alcinei Mistico
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Acta Scientiarum. Agronomy (Online)
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/56160
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.
id UEM-5_2cf349ec8470da513123cb5dc8fceb2e
oai_identifier_str oai:periodicos.uem.br/ojs:article/56160
network_acronym_str UEM-5
network_name_str Acta Scientiarum. Agronomy (Online)
repository_id_str
spelling Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori informationEstimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori informationIpomoea batatas (L.) Lam; genetical enhancement; bayes' theorem; biometry; experimental statistics.Ipomoea batatas (L.) Lam; genetical enhancement; bayes' theorem; biometry; experimental statistics.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.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.Universidade Estadual de Maringá2022-09-16info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/5616010.4025/actasciagron.v45i1.56160Acta Scientiarum. Agronomy; Vol 45 (2023): Publicação contínua; e56160Acta Scientiarum. Agronomy; v. 45 (2023): Publicação contínua; e561601807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/56160/751375154800Copyright (c) 2022 Acta Scientiarum. Agronomyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessRibeiro Valadares , Nermy Fernandes, Ana Clara Gonçalves Rodrigues , Clóvis Henrique Oliveira Guedes, Lis Lorena Melúcio Magalhães, Jailson Ramos Alves , Rayane Aguiar Andrade Júnior , Valter Carvalho deAzevedo, Alcinei Mistico2023-01-31T19:23:12Zoai:periodicos.uem.br/ojs:article/56160Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2023-01-31T19:23:12Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information
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
Ribeiro Valadares , Nermy
Ipomoea batatas (L.) Lam; genetical enhancement; bayes' theorem; biometry; experimental statistics.
Ipomoea batatas (L.) Lam; genetical enhancement; bayes' theorem; biometry; experimental statistics.
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 Ribeiro Valadares , Nermy
author_facet Ribeiro Valadares , Nermy
Fernandes, Ana Clara Gonçalves
Rodrigues , Clóvis Henrique Oliveira
Guedes, Lis Lorena Melúcio
Magalhães, Jailson Ramos
Alves , Rayane Aguiar
Andrade Júnior , Valter Carvalho de
Azevedo, Alcinei Mistico
author_role author
author2 Fernandes, Ana Clara Gonçalves
Rodrigues , Clóvis Henrique Oliveira
Guedes, Lis Lorena Melúcio
Magalhães, Jailson Ramos
Alves , Rayane Aguiar
Andrade Júnior , Valter Carvalho de
Azevedo, Alcinei Mistico
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Ribeiro Valadares , Nermy
Fernandes, Ana Clara Gonçalves
Rodrigues , Clóvis Henrique Oliveira
Guedes, Lis Lorena Melúcio
Magalhães, Jailson Ramos
Alves , Rayane Aguiar
Andrade Júnior , Valter Carvalho de
Azevedo, Alcinei Mistico
dc.subject.por.fl_str_mv Ipomoea batatas (L.) Lam; genetical enhancement; bayes' theorem; biometry; experimental statistics.
Ipomoea batatas (L.) Lam; genetical enhancement; bayes' theorem; biometry; experimental statistics.
topic Ipomoea batatas (L.) Lam; genetical enhancement; bayes' theorem; biometry; experimental statistics.
Ipomoea batatas (L.) Lam; genetical enhancement; bayes' theorem; biometry; experimental statistics.
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 2022
dc.date.none.fl_str_mv 2022-09-16
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/56160
10.4025/actasciagron.v45i1.56160
url http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/56160
identifier_str_mv 10.4025/actasciagron.v45i1.56160
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/56160/751375154800
dc.rights.driver.fl_str_mv Copyright (c) 2022 Acta Scientiarum. Agronomy
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2022 Acta Scientiarum. Agronomy
https://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 Universidade Estadual de Maringá
publisher.none.fl_str_mv Universidade Estadual de Maringá
dc.source.none.fl_str_mv Acta Scientiarum. Agronomy; Vol 45 (2023): Publicação contínua; e56160
Acta Scientiarum. Agronomy; v. 45 (2023): Publicação contínua; e56160
1807-8621
1679-9275
reponame:Acta Scientiarum. Agronomy (Online)
instname:Universidade Estadual de Maringá (UEM)
instacron:UEM
instname_str Universidade Estadual de Maringá (UEM)
instacron_str UEM
institution UEM
reponame_str Acta Scientiarum. Agronomy (Online)
collection Acta Scientiarum. Agronomy (Online)
repository.name.fl_str_mv Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br
_version_ 1799305901136412672