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: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/36429
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 modelParâmetros populacionais e seleção de genótipos de couve por inferência bayesiana em modelo linear multicaracterísticoBrassica oleracea L. var. acephala DC.Genetic parametersCrop breedingStatistical modelingCorrelationsVariance 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.Para selecionar genitores em programas de melhoramento deve-se obter os componentes de variância para estimar parâmetros genéticos e predizer valores genéticos, os quais podem ser obtidos vantajosamente pela inferência bayesiana. Quando várias características são avaliadas a inferência bayesiana pode ser utilizada em modelos multicaracterísticos. Objetivou-se obter estimativas de parâmetros genéticos, ganhos de seleção, conhecer as correlações genéticas entre as características, predizer valores genéticos e selecionar melhores genótipos de couve utilizando a abordagem bayesiana em modelo linear multicaracterístico. Foram avaliados o diâmetro do caule, altura da planta, número de brotações, número de folhas comercializáveis e massa fresca de folhas por inferência bayesiana em 22 genótipos de couve. Foi utilizado o delineamento em blocos casualizados com três repetições e quatro plantas por parcela. Verificou-se a predominância dos efeitos genéticos sobre os ambientais. As maiores estimativas de correlação foram encontradas entre a matéria fresca de folhas e as características diâmetro do caule, altura de plantas e número de folhas comercializáveis. Além das testemunhas comerciais, são indicados para o cultivo e para integrar programas de melhoramento os genótipos UFLA 11, UFLA 5, UFLA 6, UFVJM 3 e UFVJM 19. As estimativas do ganho de seleção indicaram o potencial de melhoramento para a população estudada.Editora da Universidade Estadual de Maringá (Eduem)2019-08-23T20:13:36Z2019-08-23T20:13:36Z2017-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfAZEVEDO, A. M. et al. Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model. Acta Scientiarum. Agronomy, Maringá, v. 39, n. 1, p. 25-31, Jan./Mar. 2017. DOI: 10.4025/actasciagron.v39i1.30856.http://repositorio.ufla.br/jspui/handle/1/36429Acta Scientiarum. Agronomyreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessAzevedo, Alcinei MisticoAndrade Júnior, Valter Carvalho deSantos, Albertir Aparecido dosSousa Júnior, Aderbal Soares deOliveira, Altino Júnior MendesFerreira, Marcos Aurélio Mirandaeng2023-05-26T18:49:53Zoai:localhost:1/36429Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-26T18:49:53Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model
Parâmetros populacionais e seleção de genótipos de couve por inferência bayesiana em modelo linear multicaracterístico
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
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
topic Brassica oleracea L. var. acephala DC.
Genetic parameters
Crop breeding
Statistical modeling
Correlations
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 2017-03
2019-08-23T20:13:36Z
2019-08-23T20:13:36Z
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 AZEVEDO, A. M. et al. Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model. Acta Scientiarum. Agronomy, Maringá, v. 39, n. 1, p. 25-31, Jan./Mar. 2017. DOI: 10.4025/actasciagron.v39i1.30856.
http://repositorio.ufla.br/jspui/handle/1/36429
identifier_str_mv AZEVEDO, A. M. et al. Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model. Acta Scientiarum. Agronomy, Maringá, v. 39, n. 1, p. 25-31, Jan./Mar. 2017. DOI: 10.4025/actasciagron.v39i1.30856.
url http://repositorio.ufla.br/jspui/handle/1/36429
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://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 Editora da Universidade Estadual de Maringá (Eduem)
publisher.none.fl_str_mv Editora da Universidade Estadual de Maringá (Eduem)
dc.source.none.fl_str_mv Acta Scientiarum. Agronomy
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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