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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agrobiologia. Para informações adicionais entre em contato com cnpab.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agrobiologia. |
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Data corrente: |
24/01/2011 |
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Data da última atualização: |
12/03/2015 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FERNANDES JUNIOR, P. I.; OLIVEIRA, P. J. de; RUMJANEK, N. G.; XAVIER, G. R. |
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Afiliação: |
PAULO IVAN FERNANDES JUNIOR, CPATSA; PUALO JANSEN DE OLIVEIRA, UFRRJ; NORMA GOUVEA RUMJANEK, CNPAB; GUSTAVO RIBEIRO XAVIER, CNPAB. |
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Título: |
Poly-B-hydroxybutyrate and exopolysaccharide biosynthesis by bacterial isolates from Pigeonpea Cajanus cajan (L.) Millsp. root nodules. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
Applied Biochemistry and Biotechnology, online, 05. set. 2010 |
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Idioma: |
Português |
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Palavras-Chave: |
Biopolímero; Biopolimers; Inoculant Technology; Tecnologia de inoculante. |
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Categoria do assunto: |
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Marc: |
LEADER 00645naa a2200193 a 4500
001 1874139
005 2015-03-12
008 2010 bl --- 0-- u #d
100 1 $aFERNANDES JUNIOR, P. I.
245 $aPoly-B-hydroxybutyrate and exopolysaccharide biosynthesis by bacterial isolates from Pigeonpea Cajanus cajan (L.) Millsp. root nodules.
260 $c2010
653 $aBiopolímero
653 $aBiopolimers
653 $aInoculant Technology
653 $aTecnologia de inoculante
700 1 $aOLIVEIRA, P. J. de
700 1 $aRUMJANEK, N. G.
700 1 $aXAVIER, G. R.
773 $tApplied Biochemistry and Biotechnology, online, 05. set. 2010
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Embrapa Agrobiologia (CNPAB) |
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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: |
24/10/2012 |
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Data da última atualização: |
20/02/2015 |
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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: |
RESENDE JUNIOR, M. F. R.; MUÑOZ, P.; RESENDE, M. D. V. de; GARRICK, D. J.; FERNANDO, R. L.; DAVIS, J. M.; JOKELA, E. J.; MARTIN, T. A.; PETER, G. F.; KIRST, M. |
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Afiliação: |
M. F. R. RESENDE JUNIOR, UNIVERSITY OF FLORIDA; P. MUÑOZ, UNIVERSITY OF FLORIDA; MARCOS DEON VILELA DE RESENDE, CNPF; D. J. GARRICK, IOWA STATE UNIVERSITY; R. L. FERNANDO, IOWA STATE UNIVERSITY; J. M. DAVIS, UNIVERSITY OF FLORIDA; E. J. JOKELA, UNIVERSITY OF FLORIDA; T. A. MARTIN, UNIVERSITY OF FLORIDA; G. F. PETER, UNIVERSITY OF FLORIDA; M. KIRST, UNIVERSITY OF FLORIDA. |
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Título: |
Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.) |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Genetics, v. 190, p. 1503-1510, April 2012. |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression–best linear unbiased prediction (RR–BLUP), (ii) Bayes A, (iii) Bayes Cp, and (iv) Bayesian LASSO are presented. In addition, a modified RR–BLUP (RR–BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cp, Bayes A, and RR–BLUB B had higher predictive ability than RR–BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR–BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models. MenosGenomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression–best linear unbiased prediction (RR–BLUP), (ii) Bayes A, (iii) Bayes Cp, and (iv) Bayesian LASSO are presented. In addition, a modified RR–BLUP (RR–BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cp, Bayes A, and RR–BLUB B had higher predictive ability than RR–BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR–BLUP is the assumption of equal contrib... Mostrar Tudo |
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Palavras-Chave: |
Precisão. |
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Thesagro: |
Pinus Taeda; Seleção Genética. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02528naa a2200265 a 4500
001 1937733
005 2015-02-20
008 2012 bl uuuu u00u1 u #d
100 1 $aRESENDE JUNIOR, M. F. R.
245 $aAccuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.)$h[electronic resource]
260 $c2012
520 $aGenomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression–best linear unbiased prediction (RR–BLUP), (ii) Bayes A, (iii) Bayes Cp, and (iv) Bayesian LASSO are presented. In addition, a modified RR–BLUP (RR–BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cp, Bayes A, and RR–BLUB B had higher predictive ability than RR–BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR–BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.
650 $aPinus Taeda
650 $aSeleção Genética
653 $aPrecisão
700 1 $aMUÑOZ, P.
700 1 $aRESENDE, M. D. V. de
700 1 $aGARRICK, D. J.
700 1 $aFERNANDO, R. L.
700 1 $aDAVIS, J. M.
700 1 $aJOKELA, E. J.
700 1 $aMARTIN, T. A.
700 1 $aPETER, G. F.
700 1 $aKIRST, M.
773 $tGenetics$gv. 190, p. 1503-1510, April 2012.
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