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Registro Completo |
Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
24/10/2012 |
Data da última atualização: |
20/02/2015 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
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. |
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. |
Título: |
Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.) |
Ano de publicação: |
2012 |
Fonte/Imprenta: |
Genetics, v. 190, p. 1503-1510, April 2012. |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Precisão. |
Thesagro: |
Pinus Taeda; Seleção Genética. |
Categoria do assunto: |
-- |
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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Embrapa Florestas (CNPF) |
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Registros recuperados : 28 | |
8. | | ROSADO, A. M.; ROSADO, T. B.; RESENDE JÚNIOR, M. F. R.; BHERING, L. L.; CRUZ, C. D. Ganhos genéticos preditos por diferentes métodos de seleção em progênies de Eucalyptus urophylla Pesquisa Agropecuária Brasileira, Brasília, DF, v. 44, n. 12, p. 1653-1659, dez. 2009 Título em inglês: Predicted genetic gains by various selection methods in Eucalyptus urophylla progenies.Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
Biblioteca(s): Embrapa Agroenergia; Embrapa Unidades Centrais. |
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9. | | RESENDE JUNIOR, M. F. R.; ALVES, A. A.; BARRERA SÁNCHES, C. F.; RESENDE, M. D. V. de; CRUZ, C. D. Seleção genômica ampla. In: CRUZ, C. D.; SALGADO, C. C.; BHERING, L. L. (Ed.). Genômica aplicada. Viçosa, MG: Suprema, 2013. p. 375-424.Tipo: Capítulo em Livro Técnico-Científico |
Biblioteca(s): Embrapa Agroenergia; Embrapa Florestas. |
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10. | | RESENDE JUNIOR, M. F. R.; MUÑOZ, P.; ACOSTA, J. J.; PETER, G. F.; DAVIS, J. M.; GRATTAPAGLIA, D.; RESENDE, M. D. V. de; KIRST, M. Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytologist, v. 193, p. 617-624, 2012.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
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11. | | RESENDE, M. D. V. de; RESENDE JUNIOR, M. F. R.; AGUIAR, A. M.; ABAD, J. I. M.; MISSIAGGIA, A. A.; SANSALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Computação da Seleção Genômica Ampla (GWS). Colombo: Embrapa Florestas, 2010. CD-ROM. (Embrapa Florestas. Documentos, 210).Biblioteca(s): Embrapa Florestas. |
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12. | | ALMEIDA FILHO, J. E. de; GUIMARÃES, J. F. R.; SILVA, F. F. e; RESENDE, M. D. V. de; MUÑOZ, P.; KIRST, M.; RESENDE JUNIOR, M. F. R. The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity, v. 117, p. 33-41, July 2016.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Florestas. |
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13. | | MÜLLER, B. S. F.; NEVES, L. G.; RESENDE JÚNIOR, M. F. R.; MUÑOZ, P. R.; KIRST, M.; SANTOS, P. E. T. dos; PALUDZYSZYN FILHO, E.; GRATTAPAGLIA, D. Genomic selection for growth traits in Eucalyptus benthamii and E. pellita populations using a genome-wide Eucalyptus 60K SNPs chip. In: IUFRO TREE BIOTECHNOLOGY CONFERENCE, 2015, Florence. Forests: the importance to the planet and society. [S.l.]: IBBR: ICCOM, 2015.Tipo: Artigo em Anais de Congresso |
Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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14. | | MÜLLER, B. S. F.; NEVES, L. G.; RESENDE JÚNIOR, M. F. R.; MUÑOZ, P. R.; KIRST, M.; SANTOS, P. E. T. dos; PALUDZYSZYN FILHO, E.; GRATTAPAGLIA, D. Genomic selection for growth traits in Eucalyptus benthamii and E. pellita populations using a genome-wide Eucalyptus 60K SNPs chip. In: IUFRO TREE BIOTECHNOLOGY CONFERENCE, 2015, Florence. Forests: the importance to the planet and society. [S.l.]: IBBR: ICCOM, 2015. Pen-drive.Tipo: Artigo em Anais de Congresso |
Biblioteca(s): Embrapa Florestas. |
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15. | | SOUSA, T. V.; CAIXETA, E. T.; ALKIMIM, E. R.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; SAKIYAMA, N. S.; RESENDE JÚNIOR, M. F. R. de; ZAMBOLIM, L. Population structure and genetic diversity of coffee progenies derived from Catuaí and Híbrido de Timor revealed by genome-wide SNP marker. Tree Genetics & Genomes, v. 13, n. 6, Dec. 2017.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Café. |
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16. | | AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F.; VIANA, J. M. S.; VALENTE, M. S. F.; RESENDE JUNIOR, M. F. R.; OLIVEIRA, E. J. de. New accuracy estimators for genomic selection with application in a cassava (Manihot esculenta) breeding program. Genetics and Molecular Research, v. 15, n. 4, gmr.15048838, Oct. 2016.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Florestas; Embrapa Mandioca e Fruticultura. |
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18. | | AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; VIANA, J. M. S.; VALENTE, M. S. F.; RESENDE JUNIOR, M. F. R.; MUÑOZ, P. Ridge, Lasso and Bayesian additive dominance genomic models. BMC Genetics, v. 16, art. 105, Aug. 2015. 13 p.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Florestas. |
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19. | | MUÑOZ, P. R.; RESENDE JUNIOR, M. F. R.; GEZAN, S. A.; RESENDE, M. D. V. de; CAMPOS, G. de los; KIRST, M.; HUBER, D.; PETER, G. F. Unraveling additive from nonadditive effects using genomic relationship matrices. Genetics, v. 198, p. 1759-1768, Dec. 2014.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Florestas. |
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20. | | 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. Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.) Genetics, v. 190, p. 1503-1510, April 2012.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Florestas. |
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Registros recuperados : 28 | |
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Nenhum registro encontrado para a expressão de busca informada. |
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