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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. |
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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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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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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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| Acesso ao texto completo restrito à biblioteca da Embrapa Hortaliças. Para informações adicionais entre em contato com cnph.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Hortaliças. |
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Data corrente: |
23/06/1999 |
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Data da última atualização: |
23/06/1999 |
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Autoria: |
CALBO, A. G.; MENEZES SOBRINHO, J. A. de. |
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Afiliação: |
EMBRAPA-CNPH, Brasilia, DF. |
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Título: |
Fosfina nao induz brotacao precoce do alho. |
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Ano de publicação: |
1987 |
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Fonte/Imprenta: |
Horticultura Brasileira, Brasilia, v.5, n.1, p.50, maio 1987. |
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Idioma: |
Português |
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Notas: |
Resumo. |
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Palavras-Chave: |
Brotacao precoce; Early sprouting. |
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Thesagro: |
Alho; Allium Sativum; Fosfina. |
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Thesaurus NAL: |
garlic; phosphine. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00567naa a2200217 a 4500
001 1764375
005 1999-06-23
008 1987 bl uuuu u00u1 u #d
100 1 $aCALBO, A. G.
245 $aFosfina nao induz brotacao precoce do alho.
260 $c1987
500 $aResumo.
650 $agarlic
650 $aphosphine
650 $aAlho
650 $aAllium Sativum
650 $aFosfina
653 $aBrotacao precoce
653 $aEarly sprouting
700 1 $aMENEZES SOBRINHO, J. A. de
773 $tHorticultura Brasileira, Brasilia$gv.5, n.1, p.50, maio 1987.
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Registro original: |
Embrapa Hortaliças (CNPH) |
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