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Registro Completo |
Biblioteca(s): |
Embrapa Arroz e Feijão. |
Data corrente: |
18/09/2018 |
Data da última atualização: |
18/09/2018 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
MORAIS JÚNIOR, O. P.; DUARTE, J. B.; BRESEGHELLO, F.; COELHO, A. S. G.; MORAIS, O. P.; MAGALHÃES JÚNIOR, A. M. |
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. |
Título: |
Single-step reaction norm models for genomic prediction in multienvironment recurrent selection trials. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
Crop Science, v. 58, n. 2, p. 592-607, Mar./Apr. 2018. |
ISSN: |
0011-183X |
DOI: |
10.2135/cropsci2017.06.0366 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Multienvironment prediction. |
Thesagro: |
Arroz; Melhoramento Genético Vegetal; Oryza Sativa; Progênie; Seleção Recorrente. |
Thesaurus Nal: |
Genomics; Plant breeding; Recurrent selection; Rice; Variety trials. |
Categoria do assunto: |
G Melhoramento Genético |
Marc: |
LEADER 02811naa a2200337 a 4500 001 2095887 005 2018-09-18 008 2018 bl uuuu u00u1 u #d 022 $a0011-183X 024 7 $a10.2135/cropsci2017.06.0366$2DOI 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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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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Registros recuperados : 129 | |
47. | | GUIMARÃES, C. M.; BRESEGHELLO, F.; CASTRO, A. P. de; STONE, L. F.; MORAIS JÚNIOR, O. P. de. Avaliação de arroz de terras altas do grupo indica, sob condições de irrigação adequada e de deficiência hídrica. In: CONGRESSO BRASILEIRO DE ARROZ IRRIGADO, 6., 2009, Porto Alegre. Estresses e sustentabilidade: desafios para a lavoura arrozeira: anais. Porto Alegre: Palotti, 2009. 1 CD-ROM.Tipo: Artigo em Anais de Congresso / Nota Técnica |
Biblioteca(s): Embrapa Arroz e Feijão. |
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49. | | MORAIS JÚNIOR, O. P. de; MORAIS, O. P. de; BRESEGHELLO, F.; RANGEL, P. H. N.; MAGALHÃES JUNIOR, A. M. de. Comparação de índices de seleção aplicados em seleção recorrente de arroz irrigado. In: CONGRESSO BRASILEIRO DE ARROZ IRRIGADO, 9., 2015, Pelotas. Ciência e tecnologia para otimização da orizicultura: anais. Brasília, DF: Embrapa; Pelotas: Sosbai, 2015.Tipo: Artigo em Anais de Congresso |
Biblioteca(s): Embrapa Arroz e Feijão; Embrapa Clima Temperado. |
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51. | | BARROS, M. S.; MORAIS JÚNIOR, O. P.; MELO, P. G. S.; MORAIS, O. P.; CASTRO, A. P.; BRESEGHELLO, F. Effectiveness of early-generation testing applied to upland rice breeding. Euphytica, v. 214, n. 4, article 61, Apr. 2018.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
Biblioteca(s): Embrapa Arroz e Feijão. |
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52. | | 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.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Arroz e Feijão. |
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53. | | GODINHO, V. P. C.; UTUMI, M. M.; RAMALHO, A. R.; BRESEGHELLO, F.; CASTRO, E. da M. de. Espaçamento entre fileiras e densidade de sementes para a cultivar de arroz BRS Bonança em Vilhena-RO. In: CONGRESSO DA CADEIA PRODUTIVA DE ARROZ, 1.; REUNIÃO NACIONAL DE PESQUISA DE ARROZ - RENAPA, 7., 2002, Florianópolis. Anais... Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2002. p. 350-352. (Embrapa Arroz e Feijão. Documentos, 134).Biblioteca(s): Embrapa Arroz e Feijão. |
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55. | | MOREIRA, A. M.; BORBA, T. C. de O.; CASTRO, A. P. de; BRESEGHELLO, F.; BASSINELLO, P. Z. Mapeamento de QTL para qualidade de grãos de uma população de arroz de terras altas. In: SEMINÁRIO JOVENS TALENTOS, 5., 2011, Santo Antônio de Goiás. Resumos apresentados. Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2011. p. 56. (Embrapa Arroz e Feijão. Documentos, 270).Tipo: Resumo em Anais de Congresso |
Biblioteca(s): Embrapa Arroz e Feijão. |
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57. | | 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.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Arroz e Feijão. |
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58. | | BRESEGHELLO, F.; SCHMIDT, A. B.; PESSOA FILHO, M. A. P. de C.; CATELAN, R. C.; FERREIRA, M. E. SSR marker polymorphism in recurrent selection populations CG3 and CNA6. In: CONGRESSO BRASILEIRO DA CADEIA PRODUTIVA DE ARROZ, 2.; REUNIÃO NACIONAL DE PESQUISA DE ARROZ, 8., 2006, Brasília, DF. Anais... Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2006. (Embrapa Arroz e Feijão. Documentos, 196).Tipo: Artigo em Anais de Congresso / Nota Técnica |
Biblioteca(s): Embrapa Arroz e Feijão. |
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Registros recuperados : 129 | |
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Nenhum registro encontrado para a expressão de busca informada. |
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