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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| creator | Peixoto, Marco Antônio Evangelista, Jeniffer Santana Pinto Coelho Coelho, Igor Ferreira Alves, Rodrigo Silva Laviola, Bruno Gâlveas Fonseca E Silva, Fabyano Resende, Marcos Deon Vilela de Bhering, Leonardo Lopes |
| 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. |
| doi_str_mv | 10.1371/journal.pone.0247775 |
| format | Article |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0247775</identifier><identifier>PMID: 33661980</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Alternative energy ; Alternative energy sources ; Bayesian analysis ; Biodiesel fuels ; Biofuels ; Biology and Life Sciences ; Breeding ; Computer programs ; Crop yields ; Editing ; Energy demand ; Energy sources ; Engineering and Technology ; Environmental aspects ; Environmental awareness ; Euphorbiaceae ; Experiments ; Fossil fuels ; Funding ; Genetic aspects ; Genetic diversity ; Genetic variance ; Heritability ; Markov chains ; Methodology ; Parameter estimation ; Phenotypic variations ; Physical Sciences ; Renewable energy ; Research and Analysis Methods ; Residual effects ; Reviews ; Software ; Statistical inference ; Sustainability ; Sustainable development ; Variance ; Visualization</subject><ispartof>PloS one, 2021-03, Vol.16 (3), p.e0247775-e0247775</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Peixoto et al. 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Santana Pinto Coelho</au><au>Coelho, Igor Ferreira</au><au>Alves, Rodrigo Silva</au><au>Laviola, Bruno Gâlveas</au><au>Fonseca E Silva, Fabyano</au><au>Resende, Marcos Deon Vilela de</au><au>Bhering, Leonardo Lopes</au><au>Rahimi, Mehdi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2021-03-04</date><risdate>2021</risdate><volume>16</volume><issue>3</issue><spage>e0247775</spage><epage>e0247775</epage><pages>e0247775-e0247775</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33661980</pmid><doi>10.1371/journal.pone.0247775</doi><tpages>e0247775</tpages><orcidid>https://orcid.org/0000-0002-6072-0996</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Alternative energy Alternative energy sources Bayesian analysis Biodiesel fuels Biofuels Biology and Life Sciences Breeding Computer programs Crop yields Editing Energy demand Energy sources Engineering and Technology Environmental aspects Environmental awareness Euphorbiaceae Experiments Fossil fuels Funding Genetic aspects Genetic diversity Genetic variance Heritability Markov chains Methodology Parameter estimation Phenotypic variations Physical Sciences Renewable energy Research and Analysis Methods Residual effects Reviews Software Statistical inference Sustainability Sustainable development Variance Visualization |
| title | Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy |
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