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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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Origem |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
22/12/1999 |
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Data da última atualização: |
22/12/1999 |
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Autoria: |
VAZ, C. M. P.; MACHADO, S. A. S.; MAZO, L. H.; AVACA, L. A.; CRESTANA, S. |
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Afiliação: |
EMBRAPA-CNPDIA. |
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Título: |
Adsorção do herbicida atrazina em diferentes solos. |
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Ano de publicação: |
1994 |
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Fonte/Imprenta: |
In: REUNIAO BRASILEIRA DE MANEJO E CONSERVACAO DO SOLO E DA AGUA: PEQUENA PROPRIEDADE X DESENVOLVIMENTO SUSTENTAVEL, 10., jul. 1994, Florianopolis, SC. Resumos... Florianopolis: Sociedade Brasileira de Ciencia do Solo, 1994. |
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Páginas: |
p.356-357. |
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Idioma: |
Português |
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Notas: |
Ref.122. |
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Conteúdo: |
A atrazina é um herbicida bastante utilizado no Brasil e no mundo, no controle anual de gramíneas e ervas daninhas de folhas largs em culturas de milho, sorgo, pinus e outras, em aplicação principalmente na pré-emergência. Devido à sua baixa reatividade e solubilidade em água é razoavelmente estável e persistente no meio ambiente, tendo sido encontrado nos EUA e Europa em águas superficiais e subterrâneas acima do nível máximo permitido para águas de consumo humano (3ppb). |
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Palavras-Chave: |
Controle de gramíneas; Ervas daninhas. |
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Thesagro: |
Milho; Sorgo. |
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Thesaurus NAL: |
Pinus. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01301naa a2200253 a 4500
001 1026780
005 1999-12-22
008 1994 bl --- 0-- u #d
100 1 $aVAZ, C. M. P.
245 $aAdsorção do herbicida atrazina em diferentes solos.
260 $c1994
300 $ap.356-357.
500 $aRef.122.
520 $aA atrazina é um herbicida bastante utilizado no Brasil e no mundo, no controle anual de gramíneas e ervas daninhas de folhas largs em culturas de milho, sorgo, pinus e outras, em aplicação principalmente na pré-emergência. Devido à sua baixa reatividade e solubilidade em água é razoavelmente estável e persistente no meio ambiente, tendo sido encontrado nos EUA e Europa em águas superficiais e subterrâneas acima do nível máximo permitido para águas de consumo humano (3ppb).
650 $aPinus
650 $aMilho
650 $aSorgo
653 $aControle de gramíneas
653 $aErvas daninhas
700 1 $aMACHADO, S. A. S.
700 1 $aMAZO, L. H.
700 1 $aAVACA, L. A.
700 1 $aCRESTANA, S.
773 $tIn: REUNIAO BRASILEIRA DE MANEJO E CONSERVACAO DO SOLO E DA AGUA: PEQUENA PROPRIEDADE X DESENVOLVIMENTO SUSTENTAVEL, 10., jul. 1994, Florianopolis, SC. Resumos... Florianopolis: Sociedade Brasileira de Ciencia do Solo, 1994.
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