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Registro Completo |
Biblioteca(s): |
Embrapa Café. |
Data corrente: |
08/12/2023 |
Data da última atualização: |
08/12/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
SIMIQUELI, G. F.; RESENDE, R. T.; RESENDE, M. D. V. de. |
Afiliação: |
GUILHERME FERREIRA SIMIQUELI, CORTEVA AGRISCIENCE; RAFAEL TASSINARI RESENDE, UNIVERSIDADE FEDERAL DE GOIÁS; MARCOS DEON VILELA DE RESENDE, CNPCa. |
Título: |
Maximizing multi-trait gain and diversity with genetic algorithms. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
TreeDimensional, v. 10, e023001, p. 1-14, 2023. |
DOI: |
https://doi.org/10.55746/treed.2023.03.001 |
Idioma: |
Inglês |
Conteúdo: |
Genetic gain followed by loss of diversity is not ideal in breeding programs for several species, and most studies face this problem for single traits. Thus, we propose a selection method based on Genetic Algorithms (GA) to optimize the gains for multi-traits that have a low reduction of status number (NS), which takes into account equal contributions from individuals as a result of practical issues in tree breeding. Real data were used to compare GA with a method based on a branch and bound algorithm (BB) for the single-trait problem. Simulated and real data were used to compare GA with a multi-trait method adapted from Mulamba and Mock (MM) (a genotypic ranking approach) through a range of selected individuals’ portions. The GA reached a similar gain and NS in a shorter processing time than BB. This shows the efficacy of GA in solving combinatorial NP-hard problems. In a selected portion of 1% and 2.5%, the GA had low reduction in the overall gain average and greater NS than the MM. In a selection of 20%, the GA reached the same NS as the base population and a greater gain than MM for the simulated data. The GA selected a lower number of individuals than expected at 10% and 20% selection, which contributed to a more practical breeding program that maintained the gains and without the loss of genetic diversity. Thus, GA proved to be a reliable optimization tool for multi-trait scenarios, and it can be effectively applied in tree breeding. |
Thesaurus Nal: |
Algorithms; Genetics; System optimization; Tree breeding. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159354/1/Maximizing-multi-trait-gain.pdf
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Marc: |
LEADER 02078naa a2200205 a 4500 001 2159354 005 2023-12-08 008 2023 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.55746/treed.2023.03.001$2DOI 100 1 $aSIMIQUELI, G. F. 245 $aMaximizing multi-trait gain and diversity with genetic algorithms.$h[electronic resource] 260 $c2023 520 $aGenetic gain followed by loss of diversity is not ideal in breeding programs for several species, and most studies face this problem for single traits. Thus, we propose a selection method based on Genetic Algorithms (GA) to optimize the gains for multi-traits that have a low reduction of status number (NS), which takes into account equal contributions from individuals as a result of practical issues in tree breeding. Real data were used to compare GA with a method based on a branch and bound algorithm (BB) for the single-trait problem. Simulated and real data were used to compare GA with a multi-trait method adapted from Mulamba and Mock (MM) (a genotypic ranking approach) through a range of selected individuals’ portions. The GA reached a similar gain and NS in a shorter processing time than BB. This shows the efficacy of GA in solving combinatorial NP-hard problems. In a selected portion of 1% and 2.5%, the GA had low reduction in the overall gain average and greater NS than the MM. In a selection of 20%, the GA reached the same NS as the base population and a greater gain than MM for the simulated data. The GA selected a lower number of individuals than expected at 10% and 20% selection, which contributed to a more practical breeding program that maintained the gains and without the loss of genetic diversity. Thus, GA proved to be a reliable optimization tool for multi-trait scenarios, and it can be effectively applied in tree breeding. 650 $aAlgorithms 650 $aGenetics 650 $aSystem optimization 650 $aTree breeding 700 1 $aRESENDE, R. T. 700 1 $aRESENDE, M. D. V. de 773 $tTreeDimensional$gv. 10, e023001, p. 1-14, 2023.
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7. | | RESENDE, M. D. V. de. Estatística espacial, séries temporais e competição (interação social). In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 504-558.Tipo: Capítulo em Livro Técnico-Científico |
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8. | | RESENDE, M. D. V. de. Estatística. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 56-129.Tipo: Capítulo em Livro Técnico-Científico |
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18. | | RESENDE, M. D. V. de. Matemática. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 7-55.Tipo: Capítulo em Livro Técnico-Científico |
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20. | | RESENDE, M. D. V. de. Modelos BLUP univariados multiefeitos. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 226-256.Tipo: Capítulo em Livro Técnico-Científico |
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