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| Registros recuperados : 74 | |
| 24. |  | SANTOS, J. F. dos; VIANA, J. M. S.; VILARINHO, A. A.; CÂMARA, T. M. M. Efficiency of S2 progeny selection strategies in popcorn. Crop Breeding and Applied Biotechnology, Viçosa, MG, v. 4, n. 2, p. 183-191, June 2004.| Biblioteca(s): Embrapa Arroz e Feijão; Embrapa Hortaliças. |
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| 25. | | VILARINHO, A. A.; VIANA, J. M. S.; SANTOS, J. F. dos; CÂMARA, T. M. M. Melhoramento genético vegetal: eficiência da seleção de progênies S1 e S2 de milho-pipoca, visando à produção de linhagens. Bragantia, Campinas, v. 62, n. 1, p. 9-17, 2003.| Biblioteca(s): Embrapa Hortaliças. |
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| 32. | | FARIA, V. R.; VIANA, J. M. S.; SOBREIRA, F. M.; SILVA, A. C. e. Seleção recorrente recíproca na obtenção de híbridos interpopulacionais de milho-pipoca. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 43, n. 12, p. 1749-1755, dez. 2008. Título em inglês: Reciprocal recurrent selection to obtain interpopulation hybrids of popcorn.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 33. | | CERESINO, E. B.; MINIM, V. P. R.; QUEIROZ, V. A. V.; PACHECO, C. A. P.; VIANA, J. M. S. Aceitabilidade de diferentes cultivares de milho de pipoca. In: CONGRESSO BRASILEIRO DE CIÊNCIA E TECNOLOGIA DOS ALIMENTOS, 21.; SEMINÁRIO LATINOAMERICANO E DO CARIBE DE CIÊNCIA E TECNOLOGIA DE ALIMENTOS, 15., 2008, Belo Horizonte. Anais... Belo Horizonte: Sociedade Brasileira de Ciência e Tecnologia de Alimentos, 2008. 3 p. 1 CD-ROM.| Biblioteca(s): Embrapa Tabuleiros Costeiros. |
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| 34. | | CERESINO, E. B.; MINIM, V. P. R.; QUEIROZ, V. A. V.; PACHECO, C. A. P.; VIANA, J. M. S. Aceitabilidade de diferentes cultivares de milho de pipoca. In: CONGRESSO BRASILEIRO DE CIÊNCIA E TECNOLOGIA DOS ALIMENTOS, 21.; SEMINÁRIO LATINOAMERICANO E DO CARIBE DE CIÊNCIA E TECNOLOGIA DE ALIMENTOS, 15., 2008, Belo Horizonte. Anais... Belo Horizonte: Sociedade Brasileira de Ciência e Tecnologia de Alimentos, 2008. 1 CD-ROM.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 36. | | FARIA, V. R.; VIANA, J. M. S.; MUNDIM, G. B.; SILVA, A. da C. e; CÂMARA, T. M. M. Adaptabilidade e estabilidade de populações de milho-pipoca relacionadas por ciclos de seleção. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 45, n. 12, p. 1396-1403, dez. 2010 Título em inglês: Adaptability and stability of popcorn populations related through selection cycles.| Biblioteca(s): Embrapa Unidades Centrais. |
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| Registros recuperados : 74 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
17/11/2011 |
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Data da última atualização: |
11/10/2017 |
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Tipo da produção científica: |
Nota Técnica/Nota Científica |
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Autoria: |
SILVA, F. F.; VARONA, L.; RESENDE, M. D. V. de; BUENO FILHO, J. S. S.; ROSA, G. J. M.; VIANA, J. M. S. |
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Afiliação: |
Fabyano Fonseca Silva, UFV; Luis Varona, Universidad de Zaragoza; MARCOS DEON VILELA DE RESENDE, CNPF; Júlio Sílvio S. Bueno Filho, UFLA; Guilherme J. M. Rosa, University of Wisconsin; José Marcelo Soriano Viana, UFV. |
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Título: |
A note on accuracy of Bayesian LASSO regression in GWS. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
Livestock Science, v. 142, p. 310-314, 2011. |
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DOI: |
10.1016/j.livsci.2011.09.010 |
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Idioma: |
Inglês |
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Notas: |
Short communication. |
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Conteúdo: |
Several genome wide selection (GWS) statistical methods have been proposed in the last years, and among these stands out the Bayesian LASSO (BL), which is a penalized regression method based on the regularization parameter (?) estimates. In general, the posterior mean values for ? are those that minimize the residual sum of squares (RSS) while controlling the L1 norm (absolute values) of the regression coefficients. However, another option is to use fixed values of ?, which is independent of this minimization process. Nevertheless, the most important aim of GWS is to make predictions about genomic breeding values (GBV=u) for individuals that have not been measured directly for the trait, and for this reason the parameter to maximize should be the accuracy (ru; ?u ). Thus, a question can arise as to whether such estimated ? values that minimize RSS are the same as that which maximize ru; ?u . In order to answer this question, this paper aims to provide methodological and computational resources in order to evaluate the influence of BL regularization parameter estimates on the correlation between true and estimated GBV (accuracy) depending on genetic structure of the target trait (few or many QTLs and low or medium heritability). In general, it is possible to report, on average, that GBV prediction is robust in relation to the ? estimation, since the different values for ? lead to similar accuracy values. Moreover, the fixed ? values grid request high computational costs, implying that the random ? method is more attractive, since it is much faster to use just one Gibbs sampler run, while the grid must to use one run for each fixed ? value. MenosSeveral genome wide selection (GWS) statistical methods have been proposed in the last years, and among these stands out the Bayesian LASSO (BL), which is a penalized regression method based on the regularization parameter (?) estimates. In general, the posterior mean values for ? are those that minimize the residual sum of squares (RSS) while controlling the L1 norm (absolute values) of the regression coefficients. However, another option is to use fixed values of ?, which is independent of this minimization process. Nevertheless, the most important aim of GWS is to make predictions about genomic breeding values (GBV=u) for individuals that have not been measured directly for the trait, and for this reason the parameter to maximize should be the accuracy (ru; ?u ). Thus, a question can arise as to whether such estimated ? values that minimize RSS are the same as that which maximize ru; ?u . In order to answer this question, this paper aims to provide methodological and computational resources in order to evaluate the influence of BL regularization parameter estimates on the correlation between true and estimated GBV (accuracy) depending on genetic structure of the target trait (few or many QTLs and low or medium heritability). In general, it is possible to report, on average, that GBV prediction is robust in relation to the ? estimation, since the different values for ? lead to similar accuracy values. Moreover, the fixed ? values grid request high computational costs, impl... Mostrar Tudo |
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Palavras-Chave: |
Genome wide selection; Penalized regression; SNP markers. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02366naa a2200241 a 4500
001 1906248
005 2017-10-11
008 2011 bl uuuu u00u1 u #d
024 7 $a10.1016/j.livsci.2011.09.010$2DOI
100 1 $aSILVA, F. F.
245 $aA note on accuracy of Bayesian LASSO regression in GWS.$h[electronic resource]
260 $c2011
500 $aShort communication.
520 $aSeveral genome wide selection (GWS) statistical methods have been proposed in the last years, and among these stands out the Bayesian LASSO (BL), which is a penalized regression method based on the regularization parameter (?) estimates. In general, the posterior mean values for ? are those that minimize the residual sum of squares (RSS) while controlling the L1 norm (absolute values) of the regression coefficients. However, another option is to use fixed values of ?, which is independent of this minimization process. Nevertheless, the most important aim of GWS is to make predictions about genomic breeding values (GBV=u) for individuals that have not been measured directly for the trait, and for this reason the parameter to maximize should be the accuracy (ru; ?u ). Thus, a question can arise as to whether such estimated ? values that minimize RSS are the same as that which maximize ru; ?u . In order to answer this question, this paper aims to provide methodological and computational resources in order to evaluate the influence of BL regularization parameter estimates on the correlation between true and estimated GBV (accuracy) depending on genetic structure of the target trait (few or many QTLs and low or medium heritability). In general, it is possible to report, on average, that GBV prediction is robust in relation to the ? estimation, since the different values for ? lead to similar accuracy values. Moreover, the fixed ? values grid request high computational costs, implying that the random ? method is more attractive, since it is much faster to use just one Gibbs sampler run, while the grid must to use one run for each fixed ? value.
653 $aGenome wide selection
653 $aPenalized regression
653 $aSNP markers
700 1 $aVARONA, L.
700 1 $aRESENDE, M. D. V. de
700 1 $aBUENO FILHO, J. S. S.
700 1 $aROSA, G. J. M.
700 1 $aVIANA, J. M. S.
773 $tLivestock Science$gv. 142, p. 310-314, 2011.
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