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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 Florestas. |
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Data corrente: |
08/11/2019 |
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Data da última atualização: |
08/11/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ALMEIDA FILHO, J. E. de A.; GUIMARÃES, J. F. R.; SILVA, F. F. e; RESENDE, M. D. V. de; MUÑOZ, P.; KIRST, M.; RESENDE JÚNIOR, M. F. R. de. |
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Afiliação: |
Janeo Eustáquio de Almeida Filho, Universidade Esatdual do Norte Fluminense e "Darcy Ribeiro"; João Filipi Rodrigues Guimarães, Futuragene Ltda; Fabyano Fonsceca e Silva, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; Patricio Muñoz, University of Florida; Matias Kirst, University of Florida; Marcio Fernando Ribeiro de Resende Júnior, University of Florida. |
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Título: |
Genomic prediction of additive and non-additive effects using genetic markers and pedigrees. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
G3: Genes, Genomes, Genetics, v. 9, p. 2739-2748, Aug. 2019. |
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Idioma: |
Inglês |
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Conteúdo: |
The genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits MenosThe genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and ... Mostrar Tudo |
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Palavras-Chave: |
BayesA; Genomic Prediction; Genotypic Value; GenPred; Oligogenic; Polygenic; Predição genòmica; RKHS; Shared Data Resources. |
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Thesagro: |
Genótipo. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02704naa a2200313 a 4500
001 2114084
005 2019-11-08
008 2019 bl uuuu u00u1 u #d
100 1 $aALMEIDA FILHO, J. E. de A.
245 $aGenomic prediction of additive and non-additive effects using genetic markers and pedigrees.$h[electronic resource]
260 $c2019
520 $aThe genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits
650 $aGenótipo
653 $aBayesA
653 $aGenomic Prediction
653 $aGenotypic Value
653 $aGenPred
653 $aOligogenic
653 $aPolygenic
653 $aPredição genòmica
653 $aRKHS
653 $aShared Data Resources
700 1 $aGUIMARÃES, J. F. R.
700 1 $aSILVA, F. F. e
700 1 $aRESENDE, M. D. V. de
700 1 $aMUÑOZ, P.
700 1 $aKIRST, M.
700 1 $aRESENDE JÚNIOR, M. F. R. de
773 $tG3: Genes, Genomes, Genetics$gv. 9, p. 2739-2748, Aug. 2019.
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Registro original: |
Embrapa Florestas (CNPF) |
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| Registros recuperados : 6 | |
| 1. |  | ALMEIDA FILHO, J. E. de; GUIMARÃES, J. F. R.; SILVA, F. F. e; RESENDE, M. D. V. de; MUÑOZ, P.; KIRST, M.; RESENDE JUNIOR, M. F. R. The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity, v. 117, p. 33-41, July 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas. |
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| 2. | | GUIMARÃES, J. F. R.; ALMEIDA FILHO, J. E.; RESENDE JÚNIOR, M. F.; RESENDE, M. D. V. de; SILVA, F. F. e; MUÑOZ, P.; KIRST, M. Predictive ability behavior across sites after discard of SNPS with unstable effects. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: SBMP: UFG, 2015. Resumo.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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| 3. | | PIMENTEL, A. J. B.; GUIMARÃES, J. F. R.; SOUZA, M. A. de; RESENDE, M. D. V. de; MOURA, L. M.; ROCHA, J. R. do A. S. de C.; RIBEIRO, G. Estimação de parâmetros genéticos e predição de valor genético aditivo de trigo utilizando modelos mistos. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 49, n. 11, p. 882-890, nov. 2014. Título em inglês: Estimation of genetic parameters and prediction of additive genetic value for wheat by mixed models.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 4. | | PIMENTEL, A. J. B.; GUIMARÃES, J. F. R.; SOUZA, M. A. de; RESENDE, M. D. V. de; MOURA, L. M.; ROCHA, J. R. do A. S. de C.; RIBEIRO, G. Estimação de parâmetros genéticos e predição de valor genético aditivo de trigo utilizando modelos mistos. Pesquisa Agropecuária Brasileira, Brasília, v. 49, n. 11, p. 882-890, nov. 2014.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas. |
|  |
| 5. | | ALMEIDA FILHO, J. E. de A.; GUIMARÃES, J. F. R.; SILVA, F. F. e; RESENDE, M. D. V. de; MUÑOZ, P.; KIRST, M.; RESENDE JÚNIOR, M. F. R. de. Genomic prediction of additive and non-additive effects using genetic markers and pedigrees. G3: Genes, Genomes, Genetics, v. 9, p. 2739-2748, Aug. 2019.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Florestas. |
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| 6. | | ALMEIDA FILHO, J. E.; TARDIN, F. D.; GUIMARAES, J. F. R.; RESENDE, M. D. V.; SILVA, F. F.; SIMEONE, M. L. F.; MENEZES, C. B. de; QUEIROZ, V. A. V. Multi-trait BLUP model indicates sorghum hybrids with genetic potential for agronomic and nutritional traits. Genetics and Molecular Research, Ribeirão Preto, v. 15, n. 1, p. 1-9, 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas; Embrapa Milho e Sorgo. |
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| Registros recuperados : 6 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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