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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
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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 : 48 | |
| 8. |  | NEVES, L. G.; PAPPAS JUNIOR, G. J.; PAQUALI, G.; KIRST, M.; GRATTAPAGLIA, D. Advancing to a high density gene-rich map based on single feature polymorphisms in tropical eucalyptus. In: INTERNATIONAL PLANT & ANIMAL GENOMES CONFERENCE, 17., 2009, San Diego, CA. [Proceedings...]. [S. l.: s.n.], 2009. W185| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 10. | | MUNOZ, P.; RESENDE JUNIOR, M.; RESENDE, M. D. V. de; GEZAN, S.; KIRST, M.; PETER, G. The re-discovery of the dominance variation by using the observed relationship matrix and itis implications in breeding. In: INTERNATIONAL CONFERENCE ON QUANTITATIVE GENETICS, 4., 2012, Edinburgh. Understanding Variation in Complex Traits. . [S.l.: s.n], 2012. Poster abstracts. P-367.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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| 12. | | BRONDANI, R. P. V.; GAIOTTO, F. A.; MISSIAGGIA, A. A.; KIRST, M.; GRIBELS, R.; GRATTAPAGLIA, D. Microsatellite markers for Ceiba pentandra (Bombacaceae), an endangered tree species of the amazon forest Molecular Ecology Notes, v.3, p.177-179, 2003.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 13. | | GRATTAPAGLIA, D.; NOVAES, E.; PAPPAS, G. J.; PASQUALI, G.; KIRST, M. Single feature polymorphism discovery and validation in Eucalyptus by pseudo-testcross inheritance and mapping. In: THE INTERNATIONAL CONFERENCE ON THE STATUS OF PLANT & ANIMAL GENOME RESEARCH, 16., 2008, San Diego. Final abstracts guide. San Diego, 2008, W172.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 16. | | PROSDOCIMI, P; BITTENCOURT, D.; SILVA, F. R. da; KIRST, M.; MOTA, P. C.; RECH, E. L. Spinning gland transcriptomics from two main Clades of Spiders (Order: Araneae) - insights on their molecular, anatomical and behavioral evolution. Plos One, San Francisco, v. 6, n. 6, p. 1-15, June 2011.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Amazônia Ocidental; Embrapa Recursos Genéticos e Biotecnologia. |
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| 17. | | MISSIAGGIA, A. A.; KIRST, M.; SILVEIRA, F. Q.; GAIOTTO, F. A.; BRONDANI, R. P. V.; GRIBEL, R.; GRATTAPAGLIA, D. Análise da herança de locos microsatélites marcados com fluorescência em sumaúma (Ceiba pentandra) e estimativa de fecundação cruzada em populações naturais. In: WORKSHOP DO TALENTO ESTUDANTIL DA EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA, 4., 1999, Brasília. Anais: resumos dos trabalhos. Brasília, DF: EMBRAPA-CENARGEN, 1999. p. 67.| Tipo: Resumo em Anais de Congresso | Circulação/Nível: -- - -- |
| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 18. | | RIOS, E.; RESENDE, M.; KIRST, M.; RESENDE, M. D. V. de; ALMEIDA FILHO, J. E. de; MUNOZ, P. Predictive ability of Genomic Estimated Family Values (GEFV). In: PLANT & ANIMAL GENOME CONFERENCE, 24., 2016, San Diego. [Abstracts...]. San Diego: [s.n.], 2016. Pôster P1186.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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| 19. | | NOVAES, E.; DROST, D. R.; FARMERIE, W. G.; PAPPAS JUNIOR, G. J.; GRATTAPAGLIA, D.; SEDEROFF, R. R.; KIRST, M. High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome. BMC Genomic, v.9, p. 312, 2008.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: Internacional - A |
| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| Registros recuperados : 48 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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