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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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Biblioteca |
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Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Corte; Embrapa Pantanal. |
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Data corrente: |
09/04/2008 |
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Data da última atualização: |
24/04/2008 |
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Tipo da produção científica: |
Circular Técnica |
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Autoria: |
ROSA, A. N.; ABREU, U. G. P. de. |
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Afiliação: |
Antonio Nascimento Rosa, Embrapa Gado de Corte; Urbano Gomes Pinto de, Embrapa Pantanal. |
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Título: |
Características que devem ser avaliadas para seleção ou compra de touros. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
Corumbá: Embrapa Pantanal, 2007. |
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Páginas: |
3 p. |
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Série: |
(Embrapa Pantanal. Circular Técnica, 72) |
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Idioma: |
Português |
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Notas: |
Formato eletrônico |
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Conteúdo: |
A aquisição de touros para o rebanho de cria é um aspecto extremamente importante na tomada de decisão, pois envolve, além do investimento direto nesta compra, o desempenho produtivo e reprodutivo do rebanho e o progresso genético dos animais. Assim, diferentes aspectos devem ser levados em conta quando da compra ou da seleção destes reprodutores no próprio rebanho. Em geral, salientam-se a fertilidade, a libido, funcionalidade, conformação frigorífica, características raciais e os resultados de avaliação genética, especialmente, em termos de DEP - Diferença Esperada na Progênie. |
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Palavras-Chave: |
avaliação genética; Buy; Selection; touros. |
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Thesagro: |
Compra; Fertilidade; Melhoramento Genético Animal; Seleção; Touro. |
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Thesaurus NAL: |
animal breeding; bulls. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/CPAP/56241/1/CT72.pdf
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Marc: |
LEADER 01369nam a2200289 a 4500
001 1811301
005 2008-04-24
008 2007 bl uuuu u0uu1 u #d
100 1 $aROSA, A. N.
245 $aCaracterísticas que devem ser avaliadas para seleção ou compra de touros.
260 $aCorumbá: Embrapa Pantanal$c2007
300 $a3 p.
490 $a(Embrapa Pantanal. Circular Técnica, 72)
500 $aFormato eletrônico
520 $aA aquisição de touros para o rebanho de cria é um aspecto extremamente importante na tomada de decisão, pois envolve, além do investimento direto nesta compra, o desempenho produtivo e reprodutivo do rebanho e o progresso genético dos animais. Assim, diferentes aspectos devem ser levados em conta quando da compra ou da seleção destes reprodutores no próprio rebanho. Em geral, salientam-se a fertilidade, a libido, funcionalidade, conformação frigorífica, características raciais e os resultados de avaliação genética, especialmente, em termos de DEP - Diferença Esperada na Progênie.
650 $aanimal breeding
650 $abulls
650 $aCompra
650 $aFertilidade
650 $aMelhoramento Genético Animal
650 $aSeleção
650 $aTouro
653 $aavaliação genética
653 $aBuy
653 $aSelection
653 $atouros
700 1 $aABREU, U. G. P. de
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Embrapa Pantanal (CPAP) |
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