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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| creator | 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 |
| 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: h2 = 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 CVe and CVg higher for flowering in the reduced model (CVg/CVe = 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: CVe = 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. |
| doi_str_mv | 10.1371/journal.pone.0259607 |
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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: h2 = 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 CVe and CVg higher for flowering in the reduced model (CVg/CVe = 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: CVe = 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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0259607</identifier><identifier>PMID: 35503772</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Agricultural production ; Algorithms ; Bayes Theorem ; Bayesian analysis ; Bayesian statistical decision theory ; Biology and Life Sciences ; Biometrics ; Brazil ; Cereal crops ; Crop yield ; Crop yields ; Design of experiments ; Edible Grain ; Evaluation ; Experimental design ; Experiments ; Flood predictions ; Floods ; Flowering ; Genetic improvement ; Genotype ; Genotype & phenotype ; Genotypes ; Grain ; Humidity ; Management ; Markov chains ; Mathematical models ; Modelling ; Oryza - genetics ; Parameter robustness ; Phenotype ; Plant breeding ; Plant Breeding - methods ; Research and Analysis Methods ; Rice</subject><ispartof>PloS one, 2022-05, Vol.17 (5), p.e0259607-e0259607</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 da Silva Júnior et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 da Silva Júnior et al 2022 da Silva Júnior et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c688t-670b3c654512c8df24febf37b10b274788d4fe61d7881d9e5dd3cccb456ae7c43</citedby><cites>FETCH-LOGICAL-c688t-670b3c654512c8df24febf37b10b274788d4fe61d7881d9e5dd3cccb456ae7c43</cites><orcidid>0000-0002-4200-6182</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9064078/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9064078/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35503772$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>da Silva Júnior, Antônio Carlos</creatorcontrib><creatorcontrib>Sant'Anna, Isabela de Castro</creatorcontrib><creatorcontrib>Silva Siqueira, Michele Jorge</creatorcontrib><creatorcontrib>Cruz, Cosme Damião</creatorcontrib><creatorcontrib>Azevedo, Camila Ferreira</creatorcontrib><creatorcontrib>Nascimento, Moyses</creatorcontrib><creatorcontrib>Soares, Plínio César</creatorcontrib><title>Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice</title><title>PloS one</title><addtitle>PLoS One</addtitle><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. 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For the complete model, this trait presented an estimate of the relative variation index of: CVe = 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. 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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: h2 = 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 CVe and CVg higher for flowering in the reduced model (CVg/CVe = 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: CVe = 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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35503772</pmid><doi>10.1371/journal.pone.0259607</doi><tpages>e0259607</tpages><orcidid>https://orcid.org/0000-0002-4200-6182</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Agricultural production Algorithms Bayes Theorem Bayesian analysis Bayesian statistical decision theory Biology and Life Sciences Biometrics Brazil Cereal crops Crop yield Crop yields Design of experiments Edible Grain Evaluation Experimental design Experiments Flood predictions Floods Flowering Genetic improvement Genotype Genotype & phenotype Genotypes Grain Humidity Management Markov chains Mathematical models Modelling Oryza - genetics Parameter robustness Phenotype Plant breeding Plant Breeding - methods Research and Analysis Methods Rice |
| title | Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice |
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