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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Café; Embrapa Florestas. |
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Data corrente: |
05/01/2021 |
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Data da última atualização: |
05/01/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SIMIQUELI; RESENDE, M. D. V. de. |
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Afiliação: |
Guilherme Ferreira Simiqueli, UFV; MARCOS DEON VILELA DE RESENDE, CNPF. |
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Título: |
Entropy and mutual information in genome-wide selection: the splitting of k-fold cross-validation sets and implications for tree breeding. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Tree Genetics & Genomes, v. 16, article number 37, 2020. 14 p. |
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DOI: |
https://doi.org/10.1007/s11295-020-01430-6 |
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Idioma: |
Inglês |
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Conteúdo: |
Random k-fold cross-validation in genome-wide selection (GWS) can help to estimate predictive ability (ryy^). Predictive ability tends to be higher when training, and validation sets present a high degree of kinship. However, many tree breeding populations are less genetically related to the training sets and have different levels of phenotypic diversity. Therefore, this study proposes methods of splitting k-fold cross-validation sets to optimize ryy^ estimates that are consistent with the breeding population and verify the impact of phenotypic and genotypic distribution on GWS. Using a simulated Eucalyptus trait (h2=0.5) and Pinus taeda L. data for diameter at breast height (h2=0.31), six methods were developed based on mutual information (I) and entropy (H) for measuring genetic similarity and phenotypic dissimilarity, respectively. All methods were evaluated for ryy^, bias, minimum squared error of prediction, and genomic heritability. The Pearson correlations of these parameters with the kinship coefficient, and I and H between and within training and validation sets were also estimated. Our results show that closer genetic similarity did not significantly increase ryy^ and that a lower H reduced ryy^ and overestimated genomic breeding values. Consequently, phenotypic diversity (high H) should be added to tree breeding populations to increase genetic gain and reduce bias. The new methods accurately fitted models according to the entropy of tree breeding populations and their genetic relationship to the training sets. Therefore, these methods provided usable estimates of genetic gain to produce consistent success of long-term tree breeding programs. MenosRandom k-fold cross-validation in genome-wide selection (GWS) can help to estimate predictive ability (ryy^). Predictive ability tends to be higher when training, and validation sets present a high degree of kinship. However, many tree breeding populations are less genetically related to the training sets and have different levels of phenotypic diversity. Therefore, this study proposes methods of splitting k-fold cross-validation sets to optimize ryy^ estimates that are consistent with the breeding population and verify the impact of phenotypic and genotypic distribution on GWS. Using a simulated Eucalyptus trait (h2=0.5) and Pinus taeda L. data for diameter at breast height (h2=0.31), six methods were developed based on mutual information (I) and entropy (H) for measuring genetic similarity and phenotypic dissimilarity, respectively. All methods were evaluated for ryy^, bias, minimum squared error of prediction, and genomic heritability. The Pearson correlations of these parameters with the kinship coefficient, and I and H between and within training and validation sets were also estimated. Our results show that closer genetic similarity did not significantly increase ryy^ and that a lower H reduced ryy^ and overestimated genomic breeding values. Consequently, phenotypic diversity (high H) should be added to tree breeding populations to increase genetic gain and reduce bias. The new methods accurately fitted models according to the entropy of tree breeding populations and t... Mostrar Tudo |
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Palavras-Chave: |
Genetic gain; Genome-wide selection; Mutual information. |
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Thesagro: |
Eucalipto. |
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Thesaurus Nal: |
Entropy; Eucalyptus; Pinus. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02425naa a2200229 a 4500
001 2128975
005 2021-01-05
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s11295-020-01430-6$2DOI
100 1 $aSIMIQUELI
245 $aEntropy and mutual information in genome-wide selection$bthe splitting of k-fold cross-validation sets and implications for tree breeding.$h[electronic resource]
260 $c2020
520 $aRandom k-fold cross-validation in genome-wide selection (GWS) can help to estimate predictive ability (ryy^). Predictive ability tends to be higher when training, and validation sets present a high degree of kinship. However, many tree breeding populations are less genetically related to the training sets and have different levels of phenotypic diversity. Therefore, this study proposes methods of splitting k-fold cross-validation sets to optimize ryy^ estimates that are consistent with the breeding population and verify the impact of phenotypic and genotypic distribution on GWS. Using a simulated Eucalyptus trait (h2=0.5) and Pinus taeda L. data for diameter at breast height (h2=0.31), six methods were developed based on mutual information (I) and entropy (H) for measuring genetic similarity and phenotypic dissimilarity, respectively. All methods were evaluated for ryy^, bias, minimum squared error of prediction, and genomic heritability. The Pearson correlations of these parameters with the kinship coefficient, and I and H between and within training and validation sets were also estimated. Our results show that closer genetic similarity did not significantly increase ryy^ and that a lower H reduced ryy^ and overestimated genomic breeding values. Consequently, phenotypic diversity (high H) should be added to tree breeding populations to increase genetic gain and reduce bias. The new methods accurately fitted models according to the entropy of tree breeding populations and their genetic relationship to the training sets. Therefore, these methods provided usable estimates of genetic gain to produce consistent success of long-term tree breeding programs.
650 $aEntropy
650 $aEucalyptus
650 $aPinus
650 $aEucalipto
653 $aGenetic gain
653 $aGenome-wide selection
653 $aMutual information
700 1 $aRESENDE, M. D. V. de
773 $tTree Genetics & Genomes$gv. 16, article number 37, 2020. 14 p.
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Registro original: |
Embrapa Florestas (CNPF) |
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| Registros recuperados : 514 | |
| 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 |
| Biblioteca(s): Embrapa Florestas. |
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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 |
| Biblioteca(s): Embrapa Florestas. |
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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 |
| Biblioteca(s): Embrapa Florestas. |
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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 |
| Biblioteca(s): Embrapa Florestas. |
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| Registros recuperados : 514 | |
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