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| Registros recuperados : 14 | |
| 11. |  | RANGEL, P. H. N.; BRONDANI, C.; CORDEIRO, A. C. C.; SCHIOCCHET, M. A.; YOKOYAMA, S.; BACHA, R. E.; ISHIY, T. SCSBRS 113 - Tio Taka: cultivar de arroz irrigado obtida através de seleção recorrente. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 2., 2003, Porto Seguro. Melhoramento e qualidade de vida: [anais]. Porto Seguro: SBMP, 2003.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 12. | | RANGEL, P. H. N.; BRONDANI, C.; MORAIS, O. P. de; SCHIOCCHET, M. A.; BORBA, T. C. de O.; RANGEL, P. N.; BRONDANI, R. P. V.; YOKOYAMA, S.; BACHA, R. E.; ISHIY, T. Establishment of the irrigated rice cultivar SCSBRS Tio Taka by recurrent selection. Crop Breeding and Applied Biotechnology, v. 7, n. 1, p. 103-110, Mar. 2007.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Arroz e Feijão. |
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| 13. | | RANGEL, P. H. N.; BRONDANI, C.; MORAIS, O. P. de; SCHIOCCHET, M. A.; BORBA, T. C. de O.; RANGEL, P. N.; BRONDANI, R. P. V.; FAGUNDES, P. R. R.; YOKOYAMA, S.; BACHA, R. E.; ISHIY, T. Obtenção da cultivar de arroz irrigado SCSBRS Tio Taka através de seleção recorrente. In: CONGRESSO BRASILEIRO DE ARROZ IRRIGADO, 4.; REUNIÃO DA CULTURA DO ARROZ IRRIGADO, 26., 2005, Santa Maria, RS. Anais... Santa Maria, RS: Orium, 2005. v. 1. p. 195-197.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 14. | | RANGEL, P. H. N.; CORDEIRO, A. A. C.; BRONDANI, C.; BRONDANI, R. P. V.; LOPES, S. I. G.; MORAIS, O. P. de; MIURA, L.; GONÇALVES, M. I. F.; SCHIOCCHET, M. A.; YOKOYAMA, S.; BACHA. R. E.; ISHY, T. Uso da seleção recorrente no aumento da produtividade do arroz irrigado no Brasil In: CONGRESSO BRASILEIRO DE ARROZ IRRIGADO, 3.; REUNIÃO DA CULTURA DO ARROZ IRRIGADO, 25., 2003, Balneário Camboriú. Anais... Balneário Camboriú: EPAGRI, 2003.. p. 87-89.| Biblioteca(s): Embrapa Arroz e Feijão. |
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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: |
04/10/2021 |
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Data da última atualização: |
08/12/2021 |
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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.; BRESEGHELLO, F.; DUARTE, J. B.; 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, UFG; FLAVIO BRESEGHELLO, CNPAF; JOAO BATISTA DUARTE, UFG; 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: |
Assessing prediction models for different traits in a rice population derived from a Recurrent Selection Program. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Crop Science, v. 58, n. 6, p. 2347-2359, Nov./Dec. 2018. |
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ISSN: |
0011-183X |
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DOI: |
https://doi.org/10.2135/cropsci2018.02.0087 |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic selection (GS) is a promising approach to improve rice (Oryza sativa L.) populations by using genome-wide markers for selection prior to phenotyping to estimate breeding values. In this study, our objectives were to compare certain prediction models with different struc-tures of genetic relationship and statistical approaches for relevant traits in rice and to discuss some implications for integrating GS into a recurrent selection program of irrigated rice. We assessed nine models in terms of predictive potential, using empirical data from S1:3 progenies phenotyped for eight traits with different heritabilities and genotyped with 6174 high-quality single nucleotide polymorphism markers. For all traits, marker-based models outperformed prediction based on pedigree records alone. A similar level of accuracy was observed for many models, although the level of prediction stability and prediction bias varied widely. Random forest was slightly superior for less complex traits, although with high predic-tion bias, whereas the semiparametric RKHS method (reproducing kernel Hilbert spaces) was superior for many traits, showing high stability and low bias. Bayesian variable selec-tion method Bayes Cp showed acceptable accuracy and stability for several traits and thus could be useful for genomic prediction aiming at persisting accuracy for a long-term recurrent selection. |
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Thesagro: |
Arroz; Melhoramento Genético Vegetal; Oryza Sativa; Seleção Recorrente. |
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Thesaurus NAL: |
Plant breeding; Recurrent selection; Rice. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02349naa a2200313 a 4500
001 2135012
005 2021-12-08
008 2018 bl uuuu u00u1 u #d
022 $a0011-183X
024 7 $ahttps://doi.org/10.2135/cropsci2018.02.0087$2DOI
100 1 $aMORAIS JÚNIOR, O. P.
245 $aAssessing prediction models for different traits in a rice population derived from a Recurrent Selection Program.$h[electronic resource]
260 $c2018
520 $aGenomic selection (GS) is a promising approach to improve rice (Oryza sativa L.) populations by using genome-wide markers for selection prior to phenotyping to estimate breeding values. In this study, our objectives were to compare certain prediction models with different struc-tures of genetic relationship and statistical approaches for relevant traits in rice and to discuss some implications for integrating GS into a recurrent selection program of irrigated rice. We assessed nine models in terms of predictive potential, using empirical data from S1:3 progenies phenotyped for eight traits with different heritabilities and genotyped with 6174 high-quality single nucleotide polymorphism markers. For all traits, marker-based models outperformed prediction based on pedigree records alone. A similar level of accuracy was observed for many models, although the level of prediction stability and prediction bias varied widely. Random forest was slightly superior for less complex traits, although with high predic-tion bias, whereas the semiparametric RKHS method (reproducing kernel Hilbert spaces) was superior for many traits, showing high stability and low bias. Bayesian variable selec-tion method Bayes Cp showed acceptable accuracy and stability for several traits and thus could be useful for genomic prediction aiming at persisting accuracy for a long-term recurrent selection.
650 $aPlant breeding
650 $aRecurrent selection
650 $aRice
650 $aArroz
650 $aMelhoramento Genético Vegetal
650 $aOryza Sativa
650 $aSeleção Recorrente
700 1 $aBRESEGHELLO, F.
700 1 $aDUARTE, J. B.
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 $tCrop Science$gv. 58, n. 6, p. 2347-2359, Nov./Dec. 2018.
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