| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Florestas. |
|
Data corrente: |
08/11/2019 |
|
Data da última atualização: |
08/11/2019 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
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. |
|
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. |
|
Título: |
Genomic prediction of additive and non-additive effects using genetic markers and pedigrees. |
|
Ano de publicação: |
2019 |
|
Fonte/Imprenta: |
G3: Genes, Genomes, Genetics, v. 9, p. 2739-2748, Aug. 2019. |
|
Idioma: |
Inglês |
|
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 |
|
Palavras-Chave: |
BayesA; Genomic Prediction; Genotypic Value; GenPred; Oligogenic; Polygenic; Predição genòmica; RKHS; Shared Data Resources. |
|
Thesagro: |
Genótipo. |
|
Categoria do assunto: |
G Melhoramento Genético |
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Florestas (CNPF) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Acesso ao texto completo restrito à biblioteca da Embrapa Cocais. Para informações adicionais entre em contato com cpacp.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Cocais. |
|
Data corrente: |
15/08/2016 |
|
Data da última atualização: |
15/08/2016 |
|
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
|
Autoria: |
CAVALLARI, M. M.; PINHEIRO, C. U. B.; ABREU, G. B.; FRAZAO, J. M. F.; TOLEDO, M. M.; BUOSI, T. |
|
Afiliação: |
MARCELO MATTOS CAVALLARI, CPACP; CLAÚDIO URBANO BITTENCOURT PINHEIRO, UFMA; GUILHERME BARBOSA ABREU, CPACP; JOSE MARIO FERRO FRAZAO, CPACP; MARCOS MIRANDA TOLEDO, CPACP; THIAGO BUOSI, CPACP. |
|
Título: |
Babaçu. |
|
Ano de publicação: |
2015 |
|
Fonte/Imprenta: |
In: LOPES, R.; OLIVEIRA, M. do S. P. de; CAVALLARI, M. M.; BARBIERI, R. L.; CONCEIÇÃO, L. D. H. C. S. da. (Ed.). Palmeiras nativas do Brasil. Brasília, DF: Embrapa, 2015. |
|
Páginas: |
32 p. |
|
Idioma: |
Português |
|
Conteúdo: |
Aspectos botânicos e distribuição geográfica; Taxonomia; Descrição morfológica; Attalea speciosa Mart. ex Spreng; Attalea vitrivir Zona; Hibridação; Biologia reprodutiva; Distribuição geográfica; Produção de sementes e mudas; Informações agronômicas; Germinação de sementes e estabelecimentos de plântulas; Solos; Pragas; Produtividade e sistemas de produção; Caracterização e aproveitamento alimentar; Obtenção de derivados; Mecanização do processamento; Perspectivas para o processamento; Germoplasma disponível e melhoramento genético. |
|
Palavras-Chave: |
Attalea vitrivir; Babassu. |
|
Thesagro: |
Babaçu. |
|
Thesaurus NAL: |
Attalea speciosa. |
|
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
|
Marc: |
LEADER 01265naa a2200241 a 4500
001 2050840
005 2016-08-15
008 2015 bl uuuu u00u1 u #d
100 1 $aCAVALLARI, M. M.
245 $aBabaçu.
260 $c2015
300 $a32 p.
520 $aAspectos botânicos e distribuição geográfica; Taxonomia; Descrição morfológica; Attalea speciosa Mart. ex Spreng; Attalea vitrivir Zona; Hibridação; Biologia reprodutiva; Distribuição geográfica; Produção de sementes e mudas; Informações agronômicas; Germinação de sementes e estabelecimentos de plântulas; Solos; Pragas; Produtividade e sistemas de produção; Caracterização e aproveitamento alimentar; Obtenção de derivados; Mecanização do processamento; Perspectivas para o processamento; Germoplasma disponível e melhoramento genético.
650 $aAttalea speciosa
650 $aBabaçu
653 $aAttalea vitrivir
653 $aBabassu
700 1 $aPINHEIRO, C. U. B.
700 1 $aABREU, G. B.
700 1 $aFRAZAO, J. M. F.
700 1 $aTOLEDO, M. M.
700 1 $aBUOSI, T.
773 $tIn: LOPES, R.; OLIVEIRA, M. do S. P. de; CAVALLARI, M. M.; BARBIERI, R. L.; CONCEIÇÃO, L. D. H. C. S. da. (Ed.). Palmeiras nativas do Brasil. Brasília, DF: Embrapa, 2015.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Cocais (CPACP) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
