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| Registros recuperados : 147 | |
| 62. |  | SILVA, C. A. da; SCARDUA, M. P.; MARIA, A. N.; CARNEIRO, P. C. F. Productive performance among different tilapia strains oreochromis niloticus available to farmers of the lower São Francisco, NE, Brazil. In: AQUACULTURE EUROPE, 10., 2010, Porto. Proceedings... Porto: Europeam Aquaculture Societe, 2010. 1 CD-ROM. p. 1236 Artigo em anais.| Biblioteca(s): Embrapa Tabuleiros Costeiros. |
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| 66. | | SOUZA, V. L. F. de; SILVA, R. S. F. da; SILVA, C. A. da; GASPARINO, E. Vitamina E no desempenho, características de carcaça e qualidade do presunto cozido de suínos Pesquisa Agropecuária Brasileira, Brasília, DF, v. 42, n. 4, p. 581-587, abr. 2007 Título em inglês: Vitamin E on performance, carcass characteristics and quality of pig's cooked ham.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 67. | | SILVA, C. A. da; SANTOS, S. de O.; GARCIA, C. A. B.; EUFRASIO, C. A. de S.; VIANA, R. D. Teores de arsênio, cádmio e chumbo em peixes marinhos comercializados em Macieó, AL, Aracaju, SE, Salvador, BA, Brasil. Aracaju: Embrapa Tabuleiros Costeiros, 2016. (Embrapa Tabuleiros Costeiros. Boletim de Pesquisa e Desenvolvimento, 118).| Biblioteca(s): Embrapa Tabuleiros Costeiros. |
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| 68. | | MENDES, M. C.; MATCHULA, P. H.; ROSSI, E. S.; OLIVEIRA, B. R. de; SILVA, C. A. da; SEKULA, C. R. Adubação nitrogenada em cobertura associada com densidades populacionais de híbridos de milho em espaçamento reduzido. Revista Brasileira de Milho e Sorgo, Sete Lagoas, v. 12, n. 2, p. 92-101, 2013.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 69. | | GOZZOLI, T. C.; SILVA, C. A. da; CORRADINI, E.; TEIXEIRA, E. de M.; MATTOSO, L. H. C.; MARCONCINI, J. M. Amido termoplástico reforçado com fibra de bagaço de cana. In: JORNADA CIENTÍFICA - EMBRAPA SÃO CARLOS, 2., 2010, São Carlos, SP. Anais... São Carlos: Embrapa Instrumentação Agropecuária: Embrapa Pecuária Sudeste, 2010. p. 17. (Embrapa Instrumentação Agropecuária. Documentos, 50).| Biblioteca(s): Embrapa Instrumentação. |
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| 70. | | FONTANA, C.; SANTINI-JUNIOR, L.; SILVA, C. A. da; OLIVEIRA, J. M.; TOMAZELLO-FILHO, M.; BOTOSSO, P. C. Dendroecology and wood anatomy of a rare species Dinizia jueirana-facao (Fabaceae), "Tabuleiros" Atlantic Forest, Brazil. Pesquisa Florestal Brasileira, Colombo, v. 39, (nesp), e201902043, 2019. p. 241. Edição especial dos resumos do IUFRO World Congress, 25., 2019, Curitiba.| Biblioteca(s): Embrapa Florestas. |
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| 73. | | HURTADO, S. M. C.; RESENDE, A. V. de; SILVA, C. A. da; CORAZZA, E. J.; SHIRATSUCHI, L. S. Clorofilômetro no ajuste da adubação nitrogenada em cobertura para o milho de alta produtividade. Ciência Rural, Santa Maria, v. 41, n. 6, p. 1011-1017, jun. 2011.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 74. | | HURTADO, S. M. C.; RESENDE, A. V. de; SILVA, C. A. da; CORAZZA, E. J.; SHIRATSUCHI, L. S. Clorofilômetro no ajuste da adubação nitrogenada em cobertura para o milho de alta produtividade. Ciência Rural, Santa Maria, v. 41, n. 6, p. 1011-1017, jun. 2011.| Biblioteca(s): Embrapa Cerrados. |
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| 78. | | SILVA, C. A. da; TESSIER, E.; KUTTER, V. T.; WASSERMAN, J. C.; DONARD, O. F. X.; SILVA FILHO, E. V. Mercury speciation in fish of the Cabo Frio upwelling region, SE-Brasil. Brazilian Journal of Oceanography, São Paulo, v. 59, n. 3, p. 259-266, 2011.| Biblioteca(s): Embrapa Tabuleiros Costeiros. |
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| 80. | | RESENDE, A. V. de; HURTADO, S. M. C.; SHIRATSUCHI, L. S.; CORAZZA, E. J.; SILVA, C. A. da; PALUDO, V. Mapeamento da matéria orgânica do solo e da produtividade das culturas como subsídio ao manejo sítio-específico da adubação nitrogenada. In: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 31., 2007, Gramado. Conquistas e desafios da ciência do solo brasileira: anais. Porto Alegre: SBCS, 2007. 1 CD-ROM. 5 p.| Biblioteca(s): Embrapa Cerrados. |
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| Registros recuperados : 147 | |
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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. |
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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Circulação/Nível: |
A - 2 |
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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. |
|
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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