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Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice

The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the ob...

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Autores principales: da Silva Júnior, Antônio Carlos, Sant’Anna, Isabela de Castro, Silva Siqueira, Michele Jorge, Cruz, Cosme Damião, Azevedo, Camila Ferreira, Nascimento, Moyses, Soares, Plínio César
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9064078/
https://www.ncbi.nlm.nih.gov/pubmed/35503772
http://dx.doi.org/10.1371/journal.pone.0259607
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author da Silva Júnior, Antônio Carlos
Sant’Anna, Isabela de Castro
Silva Siqueira, Michele Jorge
Cruz, Cosme Damião
Azevedo, Camila Ferreira
Nascimento, Moyses
Soares, Plínio César
author_facet da Silva Júnior, Antônio Carlos
Sant’Anna, Isabela de Castro
Silva Siqueira, Michele Jorge
Cruz, Cosme Damião
Azevedo, Camila Ferreira
Nascimento, Moyses
Soares, Plínio César
author_sort da Silva Júnior, Antônio Carlos
collection PubMed
description The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h(2) = 0.039–0.80, and 0.02–0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CV(e) and CV(g) higher for flowering in the reduced model (CV(g)/CV(e) = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CV(e) = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.
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spelling pubmed-90640782022-05-04 Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice da Silva Júnior, Antônio Carlos Sant’Anna, Isabela de Castro Silva Siqueira, Michele Jorge Cruz, Cosme Damião Azevedo, Camila Ferreira Nascimento, Moyses Soares, Plínio César PLoS One Research Article The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h(2) = 0.039–0.80, and 0.02–0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CV(e) and CV(g) higher for flowering in the reduced model (CV(g)/CV(e) = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CV(e) = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice. Public Library of Science 2022-05-03 /pmc/articles/PMC9064078/ /pubmed/35503772 http://dx.doi.org/10.1371/journal.pone.0259607 Text en © 2022 da Silva Júnior et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
da Silva Júnior, Antônio Carlos
Sant’Anna, Isabela de Castro
Silva Siqueira, Michele Jorge
Cruz, Cosme Damião
Azevedo, Camila Ferreira
Nascimento, Moyses
Soares, Plínio César
Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice
title Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice
title_full Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice
title_fullStr Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice
title_full_unstemmed Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice
title_short Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice
title_sort multi-trait and multi-environment bayesian analysis to predict the g x e interaction in flood-irrigated rice
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9064078/
https://www.ncbi.nlm.nih.gov/pubmed/35503772
http://dx.doi.org/10.1371/journal.pone.0259607
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