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| Registros recuperados : 77 | |
| 41. |  | 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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| 44. | | 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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| 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. | | 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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| 49. | | BRANQUINHO, R. G.; CASTRO, A. P. de; COLOMBARI FILHO, J. M.; HEINEMANN, A. B.; DUARTE, J. B. Poder de discriminação de genótipos e representatividade de locais utilizados na avaliação de arroz de terras altas. In: SEMINÁRIO JOVENS TALENTOS, 7., 2013, Santo Antônio de Goiás. Coletânea dos resumos apresentados. Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2013. p. 98. (Embrapa Arroz e Feijão. Documentos, 292). Pôster - pós-graduação.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 51. | | 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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| 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.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 53. | | 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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| 59. | | 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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| Registros recuperados : 77 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
26/01/2018 |
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Data da última atualização: |
26/01/2018 |
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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: |
MORAIS JÚNIOR, O. P.; DUARTE, J. B.; BRESEGHELLO, F.; COELHO, A. S. G.; BORBA, T. C. O.; AGUIAR, J. T.; NEVES, P. C. F.; MORAIS, O. P. |
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Afiliação: |
ODILON PEIXOTO MORAIS JUNIOR; JOAO BATISTA DUARTE, UFG; FLAVIO BRESEGHELLO, CNPAF; ALEXANDRE S. G. COELHO, UFG; TEREZA CRISTINA DE OLIVEIRA BORBA, CNPAF; JORDENE T. AGUIAR; PERICLES DE CARVALHO FERREIRA NEVES, CNPAF; ORLANDO PEIXOTO DE MORAIS, CNPAF. |
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Título: |
Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 16, n. 4, gmr16039849, Dec. 2017. |
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ISSN: |
1676-5680 |
|
DOI: |
10.4238/gmr16039849 |
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Idioma: |
Inglês |
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Conteúdo: |
In genomic recurrent selection programs of self-pollinated crops, additive genetic effects (breeding values) are effectively relevant for selection of superior progenies as new parents. However, considering additive and nonadditive genetic effects can improve the prediction of genome-enhanced breeding values (GEBV) of progenies, for quantitative traits. In this study, we assessed the magnitude of additive and nonadditive genetic variances for eight key traits in a rice population under recurrent selection, using marker-based relationship matrices. We then assessed the goodness-to-fit, bias, stability and accuracy of prediction for breeding values and total (additive plus nonadditive) genetic values, in five genomic best linear unbiased prediction (GBLUP) models, ignoring or not nonadditive genetic effects. The models were compared using 6174 single nucleotide polymorphisms (SNP) markers from 174 S1:3 progenies evaluated in field yield trial. We found dominance effects accounting for a substantial proportion of the total genetic variance for the key traits in rice, especially for days to flowering. In average of the traits, the component of variance additive, dominance, and epistatic contributed to about 34%, 14% and 9% for phenotypic variance. Additive genomic models, ignoring nonadditive genetic effects, showed better fit to the data and lower bias, in addition to greater stability and accuracy for predict GEBV of progenies. These results improve our understanding of the genetic architecture of the key traits in rice, evaluated in early-generation testing. Clearly, this study highlighted the advantages of additive models using genome-wide information, for genomic prediction applied to recurrent selection in a self-pollinated crop. MenosIn genomic recurrent selection programs of self-pollinated crops, additive genetic effects (breeding values) are effectively relevant for selection of superior progenies as new parents. However, considering additive and nonadditive genetic effects can improve the prediction of genome-enhanced breeding values (GEBV) of progenies, for quantitative traits. In this study, we assessed the magnitude of additive and nonadditive genetic variances for eight key traits in a rice population under recurrent selection, using marker-based relationship matrices. We then assessed the goodness-to-fit, bias, stability and accuracy of prediction for breeding values and total (additive plus nonadditive) genetic values, in five genomic best linear unbiased prediction (GBLUP) models, ignoring or not nonadditive genetic effects. The models were compared using 6174 single nucleotide polymorphisms (SNP) markers from 174 S1:3 progenies evaluated in field yield trial. We found dominance effects accounting for a substantial proportion of the total genetic variance for the key traits in rice, especially for days to flowering. In average of the traits, the component of variance additive, dominance, and epistatic contributed to about 34%, 14% and 9% for phenotypic variance. Additive genomic models, ignoring nonadditive genetic effects, showed better fit to the data and lower bias, in addition to greater stability and accuracy for predict GEBV of progenies. These results improve our understanding of the ge... Mostrar Tudo |
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Palavras-Chave: |
GBLUP models; Genetic architecture; Predictive accuracy; Variance components. |
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Thesagro: |
Arroz; Melhoramento genético vegetal; Oryza sativa; Seleção recorrente. |
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Thesaurus NAL: |
Plant breeding; quantitative traits; Rice. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/171723/1/CNPAF-2017-gmr-opmj.pdf
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|
Marc: |
LEADER 02867naa a2200361 a 4500
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008 2017 bl uuuu u00u1 u #d
022 $a1676-5680
024 7 $a10.4238/gmr16039849$2DOI
100 1 $aMORAIS JÚNIOR, O. P.
245 $aRelevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding$h[electronic resource]
260 $c2017
520 $aIn genomic recurrent selection programs of self-pollinated crops, additive genetic effects (breeding values) are effectively relevant for selection of superior progenies as new parents. However, considering additive and nonadditive genetic effects can improve the prediction of genome-enhanced breeding values (GEBV) of progenies, for quantitative traits. In this study, we assessed the magnitude of additive and nonadditive genetic variances for eight key traits in a rice population under recurrent selection, using marker-based relationship matrices. We then assessed the goodness-to-fit, bias, stability and accuracy of prediction for breeding values and total (additive plus nonadditive) genetic values, in five genomic best linear unbiased prediction (GBLUP) models, ignoring or not nonadditive genetic effects. The models were compared using 6174 single nucleotide polymorphisms (SNP) markers from 174 S1:3 progenies evaluated in field yield trial. We found dominance effects accounting for a substantial proportion of the total genetic variance for the key traits in rice, especially for days to flowering. In average of the traits, the component of variance additive, dominance, and epistatic contributed to about 34%, 14% and 9% for phenotypic variance. Additive genomic models, ignoring nonadditive genetic effects, showed better fit to the data and lower bias, in addition to greater stability and accuracy for predict GEBV of progenies. These results improve our understanding of the genetic architecture of the key traits in rice, evaluated in early-generation testing. Clearly, this study highlighted the advantages of additive models using genome-wide information, for genomic prediction applied to recurrent selection in a self-pollinated crop.
650 $aPlant breeding
650 $aquantitative traits
650 $aRice
650 $aArroz
650 $aMelhoramento genético vegetal
650 $aOryza sativa
650 $aSeleção recorrente
653 $aGBLUP models
653 $aGenetic architecture
653 $aPredictive accuracy
653 $aVariance components
700 1 $aDUARTE, J. B.
700 1 $aBRESEGHELLO, F.
700 1 $aCOELHO, A. S. G.
700 1 $aBORBA, T. C. O.
700 1 $aAGUIAR, J. T.
700 1 $aNEVES, P. C. F.
700 1 $aMORAIS, O. P.
773 $tGenetics and Molecular Research$gv. 16, n. 4, gmr16039849, Dec. 2017.
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Embrapa Arroz e Feijão (CNPAF) |
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