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| Registros recuperados : 28 | |
| 4. |  | MOTA, R. R.; MARQUES, L. F. A.; LOPES, P. S.; RESENDE, M. D. V. de; SILVA, F. G. da. Inclusão de animais oriundos da técnica de transferência de embriões, na estimação de parâmetros genéticos de bovinos da raça Simental. In: SIMPÓSIO BRASILEIRO DE MELHORAMENTO ANIMAL, 9., 2012, João Pessoa. Trabalhos. João Pessoa: Sociedade Brasileira de Melhoramento Animal, 2012. Disponibilizado online.| Biblioteca(s): Embrapa Florestas. |
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| 5. | | AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; LOPES, P. S.; GUIMARÃES, S. E. F. Regressão via componentes independentes aplicada à seleção genômica para características de carcaça em suínos. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 48, n. 6, p. 619-626, jun. 2013.| Biblioteca(s): Embrapa Florestas; Embrapa Unidades Centrais. |
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| 6. | | VERARDO, L. L.; SILVA, F. F.; VARONA, L.; RESENDE, M. D. V. de; BASTIAANSEN, J. W. M.; LOPES, P. S.; GUIMARÃES, S. E. F. Bayesian GWAS and network analysis revealed new candidate genes for number of teats in pigs. Journal of Applied Genetics, v. 56, n. 1, p. 123-132, Feb. 2015.| Biblioteca(s): Embrapa Florestas. |
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| 7. | | AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F.; RESENDE, M. D. V. de; LOPES, P. S.; GUIMARÃES, S. E. F.; GLÓRIA, L. S. Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs. Genetics and Molecular Research, Ribeirão Preto, v. 14, n. 4, p. 12217-12227, 2015.| Biblioteca(s): Embrapa Florestas. |
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| 8. | | JUNQUEIRA, V. S.; LOPES, P. S.; RESENDE, M. D. V. de; SILVA, F. F. e; LOURENÇO, D. A. L.; YOKOO, M. J. I.; CARDOSO, F. F. Impact of embryo transfer phenotypic records on large-scale beef cattle genetic evaluations. Revista Brasileira de Zootecnia, Viçosa, MG, v. 47, e20170033, 2018. 4 p.| Biblioteca(s): Embrapa Florestas; Embrapa Pecuária Sul. |
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| 9. | | SANTOS, V. S.; MARTINS FILHO, S.; RESENDE, M. D. V. de; AZEVEDO, C. F.; LOPES, P. S.; GUIMARÃES, S. E. F.; SILVA, F. F. Genomic prediction for additive and dominance effects of censored traits in pigs. Genetics and Molecular Research, v. 15, n. 4, gmr15048764, Oct. 2016.| Biblioteca(s): Embrapa Florestas. |
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| 10. | | SANTOS, V. S.; MARTINS FILHO, S.; RESENDE, M. D. V. de; AZEVEDO, C. F.; LOPES, P. S.; GUIMARAES, S. E. F.; GLORIA, L. S.; SILVA, F. F. Genomic selection for slaughter age in pigs using the Cox frailty model. Genetics and Molecular Research, Ribeirão Preto, v. 14, n. 4, p. 12616-12627, 2015.| Biblioteca(s): Embrapa Florestas. |
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| 11. | | MOTA, R. R.; MARQUES, L. F. A.; LOPES, P. S.; SILVA, L. P. da; RESENDE, M. D. V. de; TORRES, R. A. Genetic evaluation using multi-trait and random regression models in Simmental beef cattle. Genetics and Molecular Research, v. 12, n. 3, p. 2465-2480, 2013.| Biblioteca(s): Embrapa Florestas. |
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| 12. | | PAIVA, J. T.; RESENDE, M. D. V. de; RESENDE, R. T.; OLIVEIRA, H. R. de; SILVA, H. T.; CAETANO, G. C.; LOPES, P. S.; SILVA, F. F. Transgenerational epigenetic variance for body weight in meat quails. Journal of Animal Breeding and Genetics, v. 135, n. 3, p. 178-185, June 2018.| Biblioteca(s): Embrapa Florestas. |
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| 13. | | MOTA, R. R.; LOPES, P. S.; MARQUES, L. F. A.; SILVA, L. P. da; RESENDE, M. D. V. de; TORRES, R. de A. The influence of animals from embryo transfer on the genetic evaluation of growth in Simmental beef cattle by using multi-trait models. Genetics and Molecular Biology, v. 36, n. 1, p. 43-49, 2013.| Biblioteca(s): Embrapa Florestas. |
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| 14. | | MOTA, R. R.; LOPES, P. S.; MARQUES, L. F. A.; SILVA, L. P.; PESSOA, M. C.; TORRES, R. A.; RESENDE, M. D. V. de. Influence of animals obtained using embryo transfer on the genetic evaluation of growth in Simmental beef cattle with random regression models. Genetics and Molecular Research, v. 12, n. 4, p. 5889-5904, 2013.| Biblioteca(s): Embrapa Florestas. |
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| 15. | | PAIVA, J. T.; RESENDE, M. D. V. de; RESENDE, R. T.; OLIVEIRA, H. R.; SILVA, H. T.; CAETANO, G. C.; LOPES, P. S.; SILVA, F. F. Epigenética: mecanismos, herança e implicações no melhoramento animal. Archivos de Zootecnia, v. 68, n. 262, p. 304-311, 2019.| Biblioteca(s): Embrapa Florestas. |
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| 16. | | COSTA, E. V.; DINIZ, D. B.; VERONEZE, R.; RESENDE, M. D. V. de; AZEVEDO, C. F.; GUIMARÃES, S. E. F.; SILVA, F. F.; LOPES, P. S. Estimating additive and dominance variances for complex traits in pigs combining genomic and pedigree information. Genetics and Molecular Research, v. 14, n. 2, p. 6303-6311, June 2015.| Biblioteca(s): Embrapa Florestas. |
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| 17. | | PINHEIRO, V. R.; SILVA, F. F. e; GUIMARÃES, S. E. F.; RESENDE, M. D. V. de; LOPES, P. S.; CRUZ, C. D.; AZEVEDO, C. F. Mapeamento de QTL para características de crescimento de suínos por meio de modelos de regressão aleatória. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 48, n. 2, p. 190-196, fev. 2013.| Biblioteca(s): Embrapa Florestas; Embrapa Unidades Centrais. |
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| 18. | | COSTA, E. V.; VENTURA. H. T.; FIGUEIREDO, E. A. P. de; SILVA, F. F.; GLÓRIA, L. S.; GODINHO, R. M.; RESENDE, M. D. V. de; LOPES, P. S. Multi-trait and repeatability models for genetic evaluation of litter traits in pigs considering different farrowings. Revista Brasileira de Saúde e Produção Animal, Salvador, v. 17, n. 4, p. 666-676, 2016.| Biblioteca(s): Embrapa Florestas; Embrapa Suínos e Aves. |
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| 19. | | AZEVEDO, C. F.; SILVA, F. F. e; RESENDE, M. D. V. de; PETERNELLI, L. A.; GUIMARÃES, S. E. F.; LOPES, P. S. Quadrados mínimos parciais uni e multivariado aplicados na seleção genômica para características de carcaça em suínos. Ciência Rural, Santa Maria, RS, v. 43, n. 9, p. 1642-1649, set. 2013.| Biblioteca(s): Embrapa Florestas. |
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| 20. | | SILVA, F. F.; JEREZ, E. A. Z.; RESENDE, M. D. V. de; VIANA, J. M. S.; AZEVEDO, C. F.; LOPES, P. S.; NASCIMENTO, M.; LIMA, R. O. de; GUIMARÃES, S. E. F. Bayesian model combining linkage and linkage disequilibrium analysis for low density-based genomic selection in animal breeding. Journal of Applied Animal Research, v. 46, n. 1, p. 873-878, 2018.| Biblioteca(s): Embrapa Florestas. |
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| Registros recuperados : 28 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
02/12/2015 |
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Data da última atualização: |
03/01/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
|
Autoria: |
SANTOS, V. S.; MARTINS FILHO, S.; RESENDE, M. D. V. de; AZEVEDO, C. F.; LOPES, P. S.; GUIMARAES, S. E. F.; GLORIA, L. S.; SILVA, F. F. |
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Afiliação: |
V. S. SANTOS, Universidade Federal de Viçosa; S. MARTINS FILHO, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; C. F. AZEVEDO, Universidade Federal de Viçosa; P. S. LOPES, Universidade Federal de Viçosa; S. E. F. GUIMARAES, Universidade Federal de Viçosa; L. S. GLORIA, Universidade Federal de Viçosa; F. F. SILVA, Universidade Federal de Viçosa. |
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Título: |
Genomic selection for slaughter age in pigs using the Cox frailty model. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Genetics and Molecular Research, Ribeirão Preto, v. 14, n. 4, p. 12616-12627, 2015. |
|
ISBN: |
http://dx.doi.org/10.4238/2015.October.19.5 |
|
Idioma: |
Inglês |
|
Conteúdo: |
The aim of this study was to compare genomic selection methodologies using a linear mixed model and the Cox survival model. We used data from an F2 population of pigs, in which the response variable was the time in days from birth to the culling of the animal and the covariates were 238 markers [237 single nucleotide polymorphism (SNP) plus the halothane gene]. The data were corrected for fixed effects, and the accuracy of the method was determined based on the correlation of the ranks of predicted genomic breeding values (GBVs) in both models with the corrected phenotypic values. The analysis was repeated with a subset of SNP markers with largest absolute effects. The results were in agreement with the GBV prediction and the estimation of marker effects for both models for uncensored data and for normality. However, when considering censored data, the Cox model with a normal random effect (S1) was more appropriate. Since there was no agreement between the linear mixed model and the imputed data (L2) for the prediction of genomic values and the estimation of marker effects, the model S1 was considered superior as it took into account the latent variable and the censored data. Marker selection increased correlations between the ranks of predicted GBVs by the linear and Cox frailty models and the corrected phenotypic values, and 120 markers were required to increase the predictive ability for the characteristic analyzed. |
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Palavras-Chave: |
Censured data; Dado censurado; Mixed model; Modelo mixto. |
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Thesagro: |
Polimorfismo. |
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Thesaurus NAL: |
polymorphism. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/134577/1/2015-M.Deon.GMR-Genomic.pdf
|
|
Marc: |
LEADER 02247naa a2200277 a 4500
001 2030248
005 2018-01-03
008 2015 bl uuuu u00u1 u #d
100 1 $aSANTOS, V. S.
245 $aGenomic selection for slaughter age in pigs using the Cox frailty model.$h[electronic resource]
260 $c2015
520 $aThe aim of this study was to compare genomic selection methodologies using a linear mixed model and the Cox survival model. We used data from an F2 population of pigs, in which the response variable was the time in days from birth to the culling of the animal and the covariates were 238 markers [237 single nucleotide polymorphism (SNP) plus the halothane gene]. The data were corrected for fixed effects, and the accuracy of the method was determined based on the correlation of the ranks of predicted genomic breeding values (GBVs) in both models with the corrected phenotypic values. The analysis was repeated with a subset of SNP markers with largest absolute effects. The results were in agreement with the GBV prediction and the estimation of marker effects for both models for uncensored data and for normality. However, when considering censored data, the Cox model with a normal random effect (S1) was more appropriate. Since there was no agreement between the linear mixed model and the imputed data (L2) for the prediction of genomic values and the estimation of marker effects, the model S1 was considered superior as it took into account the latent variable and the censored data. Marker selection increased correlations between the ranks of predicted GBVs by the linear and Cox frailty models and the corrected phenotypic values, and 120 markers were required to increase the predictive ability for the characteristic analyzed.
650 $apolymorphism
650 $aPolimorfismo
653 $aCensured data
653 $aDado censurado
653 $aMixed model
653 $aModelo mixto
700 1 $aMARTINS FILHO, S.
700 1 $aRESENDE, M. D. V. de
700 1 $aAZEVEDO, C. F.
700 1 $aLOPES, P. S.
700 1 $aGUIMARAES, S. E. F.
700 1 $aGLORIA, L. S.
700 1 $aSILVA, F. F.
773 $tGenetics and Molecular Research, Ribeirão Preto$gv. 14, n. 4, p. 12616-12627, 2015.
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