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| Registros recuperados : 64 | |
| 10. |  | NOVAES, E.; COELHO, A. S. G.; MAMANI, E.; MISSIAGGIA, A. A.; GRATTAPAGLIA, D. Mapeamento de QTLs para crescimento e qualidade da madeira em Eucalyptus. In: CONGRESSO BRASILEIRO DE GENÉTICA, 50., 2004, Costão do Santinho, Florianópolis. Resumos... Ribeirão Preto: Sociedade Brasileira de Genética, 2004. p. 1281.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 11. | | ALVARES-DA-SILVA, O.; COELHO, A. S. G.; FERREIRA, H. D.; BRANDAO, D.; VALVA, F. D. Padrao de distribuicao espacial das populacoes de um cerrado sensu stricto do Parque Nacional das Emas, GO. In: CONGRESSO DE ECOLOGIA DO BRASIL, 3., 1996, Brasilia. Manejo de ecossistemas e mudancas globais: resumos. Brasilia: UnB, 1996. p.219.| Biblioteca(s): Embrapa Cerrados. |
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| 13. | | FERREIRA, M. A. J. da F.; QUEIRÓZ, M. A. de; VENCOVSKY, R.; DUARTE, J. B.; COELHO, A. S. G. Componentes da variância genética em melancia. Horticultura Brasileira, v. 23, n. 2, p. 423, ago. 2005. Suplemento. Edição dos resumos: CONGRESSO BRASILEIRO DE OLERICULTURA, 45.; CONGRESSO BRASILEIRO DE FLORICULTURA E PLANTAS ORNAMENTAIS, 15.; CONGRESSO BRASILEIRO DE CULTURA DE TECIDOS DE PLANTAS, 2., 2005, Fortaleza, CE.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 14. | | MORAIS JÚNIOR, O. P.; DUARTE, J. B.; BRESEGHELLO, F.; COELHO, A. S. G.; MORAIS, O. P.; MAGALHÃES JÚNIOR, A. M. Single-step reaction norm models for genomic prediction in multienvironment recurrent selection trials. Crop Science, v. 58, n. 2, p. 592-607, Mar./Apr. 2018.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 15. | | ZUCCHI, M. I.; BRONDANI, R. P. V.; PINHEIRO, J. B.; CHAVES, L. J.; COELHO, A. S. G.; VENCOVSKY, R. Genetic structure and gene flow in Eugenia dysenterica DC in the Brazilian Cerrado utilizing SSR markers. Genetics and Molecular Biology, v. 26, n. 4, p. 449-457, dez. 2003.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 16. | | CRISPIM, B. C. F.; RÉSIO, L.; BUENO, L. G.; COELHO, A. S. G.; MELLO, R. N. de. Identificação de genitores para resistência à Magnaporthe oryzae em arroz utilizando-se a análise AMMI. In: SEMINÁRIO JOVENS TALENTOS, 5., 2011, Santo Antônio de Goiás. Resumos apresentados. Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2011. p. 51. (Embrapa Arroz e Feijão. Documentos, 270).| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 18. | | RIZZO, J. A.; FONSECA, A. S.; VALVA, F. A.; COELHO, A. S. G.; SILVA, M. J.; VIANA, M. V. L. Ecogenetica de Hancornia speciosa Gomez .I. Padrao de distribuicao especial de Hancornia speciosa var. gardeneri e var. pubescens. In: CONGRESSO NACIONAL DE BOTANICA, 47., 1996, Nova Friburgo. Resumos. Nova Friburgo: Sociedade Botanica do Brasil, 1996. p.360.| Biblioteca(s): Embrapa Cerrados. |
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| 19. | | BLANCO, A. J. V.; PEREIRA, M. F.; COELHO, A. S. G.; CHAVES, L. J.; CIAMPI, A. Y.; MIRANDA, D. D. Magnitude e distribuição da diversidade genética em populações naturais de araticunzeiro (Annona crassiflora Mart.). In: CONGRESSO BRASILEIRO DE GENÉTICA, 52., 2006, Foz do Iguaçu, PR. Resumos... Ribeirão Preto, SP: Sociedade Brasileira de Genética, 2006. p. 1043.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| Registros recuperados : 64 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
18/09/2018 |
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Data da última atualização: |
18/09/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
MORAIS JÚNIOR, O. P.; DUARTE, J. B.; BRESEGHELLO, F.; COELHO, A. S. G.; MORAIS, O. P.; MAGALHÃES JÚNIOR, A. M. |
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Afiliação: |
ODILON PEIXOTO MORAIS JUNIOR, UFG; JOAO BATISTA DUARTE, UFG; FLAVIO BRESEGHELLO, CNPAF; ALEXANDRE S. G. COELHO, UFG; ORLANDO PEIXOTO DE MORAIS, CNPAF; ARIANO MARTINS DE MAGALHAES JUNIOR, CPACT. |
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Título: |
Single-step reaction norm models for genomic prediction in multienvironment recurrent selection trials. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Crop Science, v. 58, n. 2, p. 592-607, Mar./Apr. 2018. |
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ISSN: |
0011-183X |
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DOI: |
10.2135/cropsci2017.06.0366 |
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Idioma: |
Inglês |
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Conteúdo: |
In recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bias of phenotypic prediction. Within-cycle data were sufficient for accurate prediction of untested progenies, even in untested environments. We concluded that the RN-HBLUP model, with the comprehensive structure, could be useful in improving the prediction accuracy of quantitative traits in RGS programs. MenosIn recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bi... Mostrar Tudo |
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Palavras-Chave: |
Multienvironment prediction. |
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Thesagro: |
Arroz; Melhoramento Genético Vegetal; Oryza Sativa; Progênie; Seleção Recorrente. |
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Thesaurus NAL: |
Genomics; Plant breeding; Recurrent selection; Rice; Variety trials. |
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Categoria do assunto: |
G Melhoramento Genético |
|
Marc: |
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100 1 $aMORAIS JÚNIOR, O. P.
245 $aSingle-step reaction norm models for genomic prediction in multienvironment recurrent selection trials.$h[electronic resource]
260 $c2018
520 $aIn recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bias of phenotypic prediction. Within-cycle data were sufficient for accurate prediction of untested progenies, even in untested environments. We concluded that the RN-HBLUP model, with the comprehensive structure, could be useful in improving the prediction accuracy of quantitative traits in RGS programs.
650 $aGenomics
650 $aPlant breeding
650 $aRecurrent selection
650 $aRice
650 $aVariety trials
650 $aArroz
650 $aMelhoramento Genético Vegetal
650 $aOryza Sativa
650 $aProgênie
650 $aSeleção Recorrente
653 $aMultienvironment prediction
700 1 $aDUARTE, J. B.
700 1 $aBRESEGHELLO, F.
700 1 $aCOELHO, A. S. G.
700 1 $aMORAIS, O. P.
700 1 $aMAGALHÃES JÚNIOR, A. M.
773 $tCrop Science$gv. 58, n. 2, p. 592-607, Mar./Apr. 2018.
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