Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy.

Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated me...

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Autores principales:	Marco Antônio Peixoto, Jeniffer Santana Pinto Coelho Evangelista, Igor Ferreira Coelho, Rodrigo Silva Alves, Bruno Gâlveas Laviola, Fabyano Fonseca E Silva, Marcos Deon Vilela de Resende, Leonardo Lopes Bhering
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/14e291f520f24a988a887a159d98bb61
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spelling	oai:doaj.org-article:14e291f520f24a988a887a159d98bb612021-11-25T06:23:47ZMultiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy.1932-620310.1371/journal.pone.0247775https://doaj.org/article/14e291f520f24a988a887a159d98bb612021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0247775https://doaj.org/toc/1932-6203Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low (ρg ≤ 0.33), moderate (0.34 ≤ ρg ≤ 0.66), and high magnitude (ρg ≥ 0.67) were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials.Marco Antônio PeixotoJeniffer Santana Pinto Coelho EvangelistaIgor Ferreira CoelhoRodrigo Silva AlvesBruno Gâlveas LaviolaFabyano Fonseca E SilvaMarcos Deon Vilela de ResendeLeonardo Lopes BheringPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 3, p e0247775 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marco Antônio Peixoto Jeniffer Santana Pinto Coelho Evangelista Igor Ferreira Coelho Rodrigo Silva Alves Bruno Gâlveas Laviola Fabyano Fonseca E Silva Marcos Deon Vilela de Resende Leonardo Lopes Bhering Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy.
description	Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low (ρg ≤ 0.33), moderate (0.34 ≤ ρg ≤ 0.66), and high magnitude (ρg ≥ 0.67) were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials.
format	article
author	Marco Antônio Peixoto Jeniffer Santana Pinto Coelho Evangelista Igor Ferreira Coelho Rodrigo Silva Alves Bruno Gâlveas Laviola Fabyano Fonseca E Silva Marcos Deon Vilela de Resende Leonardo Lopes Bhering
author_facet	Marco Antônio Peixoto Jeniffer Santana Pinto Coelho Evangelista Igor Ferreira Coelho Rodrigo Silva Alves Bruno Gâlveas Laviola Fabyano Fonseca E Silva Marcos Deon Vilela de Resende Leonardo Lopes Bhering
author_sort	Marco Antônio Peixoto
title	Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy.
title_short	Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy.
title_full	Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy.
title_fullStr	Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy.
title_full_unstemmed	Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy.
title_sort	multiple-trait model through bayesian inference applied to jatropha curcas breeding for bioenergy.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/14e291f520f24a988a887a159d98bb61
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_version_	1718413725970464768

Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy.

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