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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
21/03/2022 |
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Data da última atualização: |
12/07/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
DIAS, K. O. G.; SANTOS, J. P. R. dos; KRAUSE, M. D.; PIEPHO, H.-P.; GUIMARAES, L. J. M.; PASTINA, M. M.; GARCIA, A. A. F. |
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Afiliação: |
KAIO O. G. DIAS, Universidade Federal de Viçosa; JHONATHAN P. R. DOS SANTOS, Escola Superior de Agricultura Luiz de Queiroz; MATHEUS D. KRAUSE, Iowa State University; HANS-PETER PIEPHO, University of Hohenheim; LAURO JOSE MOREIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS; ANTONIO A. F. GARCIA, Escola Superior de Agricultura Luiz de Queiroz. |
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Título: |
Leveraging probability concepts for cultivar recommendation in multi?environment trials. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Theoretical and Applied Genetics, v. 135, n. 4, p. 1385-1399, 2022. |
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DOI: |
https://doi.org/10.1007/s00122-022-04041-y |
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Idioma: |
Inglês |
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Conteúdo: |
Statistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate ofspring, and obtain highly productive genotypes for target environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the NoU-Turn sampler algorithm to get Hamiltonian Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our fndings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specifc adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defned intensity of selection that results in a more informed decision-making process toward cultivar recommendation in multi-environment trials. |
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Palavras-Chave: |
Modelo Bayesiano; Modelo misto; Previsão genômica; Regressão de parâmetro. |
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Thesagro: |
Genótipo; Melhoramento Genético Vegetal; Variedade. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
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001 2141063
005 2022-07-12
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s00122-022-04041-y$2DOI
100 1 $aDIAS, K. O. G.
245 $aLeveraging probability concepts for cultivar recommendation in multi?environment trials.$h[electronic resource]
260 $c2022
520 $aStatistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate ofspring, and obtain highly productive genotypes for target environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the NoU-Turn sampler algorithm to get Hamiltonian Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our fndings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specifc adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defned intensity of selection that results in a more informed decision-making process toward cultivar recommendation in multi-environment trials.
650 $aGenótipo
650 $aMelhoramento Genético Vegetal
650 $aVariedade
653 $aModelo Bayesiano
653 $aModelo misto
653 $aPrevisão genômica
653 $aRegressão de parâmetro
700 1 $aSANTOS, J. P. R. dos
700 1 $aKRAUSE, M. D.
700 1 $aPIEPHO, H.-P.
700 1 $aGUIMARAES, L. J. M.
700 1 $aPASTINA, M. M.
700 1 $aGARCIA, A. A. F.
773 $tTheoretical and Applied Genetics$gv. 135, n. 4, p. 1385-1399, 2022.
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Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
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