Estimation of genetic parameters and selection gains for sweet potato using Bayesian inference with a priori information
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
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 |