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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. |
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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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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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Registro original: |
Embrapa Florestas (CNPF) |
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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
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Biblioteca(s): |
Embrapa Hortaliças. |
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Data corrente: |
26/09/2006 |
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Data da última atualização: |
26/09/2006 |
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Autoria: |
CORREIA, L. Q.; FERREIRA, P. T. O.; DUSI, A. N.; INOUE-NAGATA, A. K. |
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Afiliação: |
Embrapa Hortaliças, Brasília, DF. |
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Título: |
Análise espacial e temporal da ocorrência de begomovírus em tomateiro no DF. |
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Ano de publicação: |
2006 |
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Fonte/Imprenta: |
Fitopatologia Brasileira, Brasília, DF, v. 31, p. S245, ago. 2006. |
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Idioma: |
Português |
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Notas: |
Suplemento. Resumo 0494. Trabalho apresentado no 39. Congresso Brasileiro de Fitopatologia, 2006, Salvador. |
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Palavras-Chave: |
Análise espacial; Análise temporal. |
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Thesagro: |
Lycopersicon Esculentum; Tomate. |
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Thesaurus NAL: |
Begomovirus. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00736naa a2200217 a 4500
001 1779557
005 2006-09-26
008 2006 bl uuuu u00u1 u #d
100 1 $aCORREIA, L. Q.
245 $aAnálise espacial e temporal da ocorrência de begomovírus em tomateiro no DF.
260 $c2006
500 $aSuplemento. Resumo 0494. Trabalho apresentado no 39. Congresso Brasileiro de Fitopatologia, 2006, Salvador.
650 $aBegomovirus
650 $aLycopersicon Esculentum
650 $aTomate
653 $aAnálise espacial
653 $aAnálise temporal
700 1 $aFERREIRA, P. T. O.
700 1 $aDUSI, A. N.
700 1 $aINOUE-NAGATA, A. K.
773 $tFitopatologia Brasileira, Brasília, DF$gv. 31, p. S245, ago. 2006.
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Embrapa Hortaliças (CNPH) |
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