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| Registros recuperados : 77 | |
| 42. |  | CAVALCANTE, H. C.; NEVES, P. de C. F.; TAILLEBOIS, J.; DUARTE, J. B. Uso da seleção massal em arroz, com incremento na aptidão fêmea à produção de sementes híbridas. In: SEMINÁRIO JOVENS TALENTOS, 12., 2018, Santo Antônio de Goiás. Resumos. Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2018. p. 62. (Embrapa Arroz e Feijão. Eventos técnicos & científicos, 2)| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 43. | | 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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| 44. | | COSTA NETO, G. M. F.; DUARTE, J. B.; HEINEMANN, A. B.; CASTRO, A. P. de; ANTOLIN, L. A. S. Simulação de interação "genótipo x ambiente" para arroz de terras altas em cenários de aumento da temperatura do ar. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: UFG: SBMP, 2015.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 45. | | 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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| 46. | | OLIVEIRA, J. P. de; MOREIRA JUNIOR, W. N.; DUARTE, J. B.; CHAVES, L. J.; PINHEIRO, J. B. Genotype-environment interaction in maize hybrids: an application of the AMMI model. Crop Breeding and Applied Biotechnology, Londrina, v. 3, n. 2, p. 185-192, Sept. 2003.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 47. | | OLIVEIRA, J. P. de; CHAVES, L. J.; DUARTE, J. B.; RIBEIRTO, K. de O.; BRASIL, E. M. Heterosis for oil content in maize populations and hybrids of high quality protein. Crop breeding and applied biotechnology, Londrina, v. 6, n. 2, p. 113-120, June 2006.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 48. | | DAVI, G. S.; DUARTE, J. B.; SILVA, A. R. da; GUIMARAES, L. J. M.; GUIMARAES, P. E. Capacidade de combinação para produtividade de grãos entre linhagens elite de milho. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: UFG: SBMP, 2015.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 55. | | DIAS, M. J. D.; TONIOLLO, G. H.; DIAS, D. S. de O.; DUARTE, J. B.; BORGES, G. T. Efeito do fotoperíodo artificial e diferentes níveis de energia na dieta no pós parto sobre o desempenho reprodutivo de cabras leiteira primiparas. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 40., 2003, Santa Maria, RS. Otimizando a produção animal: anais. Santa Maria: Sociedade Brasileira de Zootecnia, 2003. 4 f. 1 CD ROM.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 57. | | PACHECO, R. M.; DUARTE, J. B.; SOUZA, P. I. M. de; SILVA, S. A. da; NUNES JUNIOR, J. Key locations for soybean genotype assessment in Central Brazil. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 44, n. 5, p. 478-486, maio 2009 Título em português: Locais-chave para avaliação de genótipos de soja na Região Central do Brasil.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Unidades Centrais. |
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| 58. | | BRANQUINHO, R. G.; DUARTE, J. B.; SOUZA, P. I. M. de; SILVA NETO, S. P. da; PACHECO, R. M. Estratificação ambiental e otimização de rede de ensaios de genótipos de soja no Cerrado. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 49, n. 10, p. 783-795, out. 2014.| Biblioteca(s): Embrapa Cerrados; Embrapa Unidades Centrais. |
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| 59. | | MORAIS JÚNIOR, O. P.; BRESEGHELLO, F.; DUARTE, J. B.; MORAIS, O. P.; RANGEL, P. H. N.; COELHO, A. S. G. Effectiveness of recurrent selection in irrigated rice breeding. Crop Science, v. 57, n. 6, p. 3043-3058, Nov./Dec. 2017.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| Registros recuperados : 77 | |
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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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