Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model

Detalhes bibliográficos
Autor(a) principal: Azevedo, Alcinei Mistico
Data de Publicação: 2017
Outros Autores: Andrade júnior, Valter Carvalho de, Santos, Albertir Aparecido dos, Sousa Júnior, Aderbal Soares de, Oliveira, Altino Júnior Mendes, Ferreira, Marcos Aurélio Miranda
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/30856
Resumo:  Variance components must be obtained to estimate genetic parameters and predict breeding values. This information can be obtained through Bayesian inference. When multiple traits are evaluated, Bayesian inference can be used in multi-trait models. The objective of this study was to obtain estimates of genetic parameters, gains with selection, and genetic correlations among traits. Likewise, we aim to predict the genetic values and select the best kale genotypes using the Bayesian approach in a multi-trait linear model. The following traits were evaluated: stem diameter, plant height, number of shoots, number of marketable leaves and fresh weight of leaves using Bayesian inference in 22 kale genotypes. The experiment consisted of a randomized block design with three replications and four plants per plot. Genetic effects predominated over environmental effects. The highest correlation estimates were found between the fresh weight of leaves and stem diameter and between the plant height and number of marketable leaves. The following commercial cultivars and genotypes are recommended for cultivation and to integrate into breeding programs: UFLA 11, UFLA 5, UFLA 6, UFVJM 3 and UFVJM 19. The estimates of the gain with selection indicate the potential for improvement of the studied population. 
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spelling Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear modelBrassica oleracea L. var. acephala DC.genetic parameterscrop breedingstatistical modelingcorrelationsMelhoramento genético de plantas Variance components must be obtained to estimate genetic parameters and predict breeding values. This information can be obtained through Bayesian inference. When multiple traits are evaluated, Bayesian inference can be used in multi-trait models. The objective of this study was to obtain estimates of genetic parameters, gains with selection, and genetic correlations among traits. Likewise, we aim to predict the genetic values and select the best kale genotypes using the Bayesian approach in a multi-trait linear model. The following traits were evaluated: stem diameter, plant height, number of shoots, number of marketable leaves and fresh weight of leaves using Bayesian inference in 22 kale genotypes. The experiment consisted of a randomized block design with three replications and four plants per plot. Genetic effects predominated over environmental effects. The highest correlation estimates were found between the fresh weight of leaves and stem diameter and between the plant height and number of marketable leaves. The following commercial cultivars and genotypes are recommended for cultivation and to integrate into breeding programs: UFLA 11, UFLA 5, UFLA 6, UFVJM 3 and UFVJM 19. The estimates of the gain with selection indicate the potential for improvement of the studied population. Universidade Estadual de Maringá2017-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPesquisa de campoapplication/pdfapplication/ziphttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/3085610.4025/actasciagron.v39i1.30856Acta Scientiarum. Agronomy; Vol 39 No 1 (2017); 25-31Acta Scientiarum. Agronomy; v. 39 n. 1 (2017); 25-311807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/30856/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/30856/751375144530Copyright (c) 2017 Acta Scientiarum. Agronomyinfo:eu-repo/semantics/openAccessAzevedo, Alcinei MisticoAndrade júnior, Valter Carvalho deSantos, Albertir Aparecido dosSousa Júnior, Aderbal Soares deOliveira, Altino Júnior MendesFerreira, Marcos Aurélio Miranda2022-02-20T21:47:24Zoai:periodicos.uem.br/ojs:article/30856Revistahttp://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:2022-02-20T21:47:24Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model
title Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model
spellingShingle Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model
Azevedo, Alcinei Mistico
Brassica oleracea L. var. acephala DC.
genetic parameters
crop breeding
statistical modeling
correlations
Melhoramento genético de plantas
title_short Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model
title_full Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model
title_fullStr Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model
title_full_unstemmed Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model
title_sort Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model
author Azevedo, Alcinei Mistico
author_facet Azevedo, Alcinei Mistico
Andrade júnior, Valter Carvalho de
Santos, Albertir Aparecido dos
Sousa Júnior, Aderbal Soares de
Oliveira, Altino Júnior Mendes
Ferreira, Marcos Aurélio Miranda
author_role author
author2 Andrade júnior, Valter Carvalho de
Santos, Albertir Aparecido dos
Sousa Júnior, Aderbal Soares de
Oliveira, Altino Júnior Mendes
Ferreira, Marcos Aurélio Miranda
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Azevedo, Alcinei Mistico
Andrade júnior, Valter Carvalho de
Santos, Albertir Aparecido dos
Sousa Júnior, Aderbal Soares de
Oliveira, Altino Júnior Mendes
Ferreira, Marcos Aurélio Miranda
dc.subject.por.fl_str_mv Brassica oleracea L. var. acephala DC.
genetic parameters
crop breeding
statistical modeling
correlations
Melhoramento genético de plantas
topic Brassica oleracea L. var. acephala DC.
genetic parameters
crop breeding
statistical modeling
correlations
Melhoramento genético de plantas
description  Variance components must be obtained to estimate genetic parameters and predict breeding values. This information can be obtained through Bayesian inference. When multiple traits are evaluated, Bayesian inference can be used in multi-trait models. The objective of this study was to obtain estimates of genetic parameters, gains with selection, and genetic correlations among traits. Likewise, we aim to predict the genetic values and select the best kale genotypes using the Bayesian approach in a multi-trait linear model. The following traits were evaluated: stem diameter, plant height, number of shoots, number of marketable leaves and fresh weight of leaves using Bayesian inference in 22 kale genotypes. The experiment consisted of a randomized block design with three replications and four plants per plot. Genetic effects predominated over environmental effects. The highest correlation estimates were found between the fresh weight of leaves and stem diameter and between the plant height and number of marketable leaves. The following commercial cultivars and genotypes are recommended for cultivation and to integrate into breeding programs: UFLA 11, UFLA 5, UFLA 6, UFVJM 3 and UFVJM 19. The estimates of the gain with selection indicate the potential for improvement of the studied population. 
publishDate 2017
dc.date.none.fl_str_mv 2017-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Pesquisa de campo
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/30856
10.4025/actasciagron.v39i1.30856
url http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/30856
identifier_str_mv 10.4025/actasciagron.v39i1.30856
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/30856/pdf
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/30856/751375144530
dc.rights.driver.fl_str_mv Copyright (c) 2017 Acta Scientiarum. Agronomy
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2017 Acta Scientiarum. Agronomy
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/zip
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 39 No 1 (2017); 25-31
Acta Scientiarum. Agronomy; v. 39 n. 1 (2017); 25-31
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
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