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| Registros recuperados : 90 | |
| 26. |  | AZEVEDO, C. F. de; MOURA, A. de A. A.; LOBO, R. N. B.; MODESTO, E. C.; MARTINS FILHO, R. Avaliação de fatores não genéticos sobre características de peso em bovinos Nelore e Guzerá no Estado do Rio Grande do Norte. Revista Ciência Agronômica, Fortaleza, v. 36, n. 2, p. 227-236, maio/ago., 2005.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 27. | | SUELA, M. M.; LIMA, L. P.; AZEVEDO, C. F.; RESENDE, M. D. V. de; NASCIMENTO, M.; SILVA, F. F. e. Combined index of genomic prediction methods applied to productivity traits in rice. Ciência Rural, Santa Maria, v. 49, n. 6, e20181008, June 2019. 9 p.| Biblioteca(s): Embrapa Florestas. |
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| 29. | | 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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| 30. | | TEODORO, P. E.; AZEVEDO, C. F.; FARIAS, F. J. C.; ALVES, R. S.; PEIXOTO, L. de A.; RIBEIRO, L. P.; CARVALHO, L. P. de; BHERING, L. L. Adaptability of cotton (Gossypium hirsutum) genotypes analysed using a Bayesian AMMI model. Crop and Pasture Science, v. 70, n. 7, p. 615-621, 2019.| Biblioteca(s): Embrapa Algodão. |
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| 31. | | TEODORO, P. E.; FARIAS, F. J. C.; CARVALHO, L. P. de; RIBEIRO, L. P.; NASCIMENTO, M.; AZEVEDO, C. F.; CRUZ, C. D.; BHERING, L. L. Adaptability and stability of cotton genotypes regarding fiber yield and quality traits. Crop Science, v. 59, p. 518?524, March?April 2019.| Biblioteca(s): Embrapa Algodão. |
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| 32. | | RESENDE, R. T.; RESENDE, M. D. V.; SILVA, F. F.; AZEVEDO, C. F.; TAKAHASHI, E. K.; SILVA JUNIOR, O. B.; GRATTAPAGLIA, D. Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model. Heredity, v. 119, p. 245-255, 2017.| Biblioteca(s): Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
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| 33. | | PEREIRA, V. C. S.; DIAS, R. V.; AZEVEDO, C. F. e O.; PEREIRA, I. S.; HONDA, L. S.; PINHEIRO, E. F. M.; CAMPOS, D. V. B. de. Agregação do solo sob diferentes usos e cobertura vegetal no bioma Cerrado. In: CONGRESSO LATINO-AMERICANO DE CIÊNCIA DO SOLO, 23.; CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 38., 2023, Florianópolis. Anais [...]. Florianópolis: Epagri, 2023. p. 949. Ref. ID 1339.| Biblioteca(s): Embrapa Solos. |
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| 37. | | AZEVEDO, C. F.; SILVA, F. F. e; RIBEIRO, N. B.; SILVA, D. J. H. da; CECON, P. R.; BARILI, L. D.; PINHEIRO, V. R. Classificação multivariada de curvas de progresso da requeima do tomateiro entre acessos do Banco de Germoplasma de Hortaliças da UFV. Ciência Rural, Santa Maria, v. 42, n. 3, p. 414-417, mar. 2012.| Biblioteca(s): Embrapa Hortaliças. |
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| 39. | | SUELA, M. M.; AZEVEDO, C. F.; NASCIMENTO, A. C. C.; MOMEN, M.; OLIVEIRA, A. C. B. de; CAIXETA, E. T.; MOROTA, G.; NASCIMENTO, M. Genome-wide association study for morphological, physiological, and productive traits in Coffea arabica using structural equation models. Tree Genetics & Genomes, v. 19, n. 3, 2023. 17 p.| Biblioteca(s): Embrapa Café. |
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| 40. | | GOIS, I. B.; BORÉM, A.; CRISTOFANI-YALY, M.; RESENDE, M. D. V. de; AZEVEDO, C. F.; BASTIANEL, M.; NOVELLI, V. M.; MACHADO, M. A. Genome wide selection in citrus breeding. Genetics and Molecular Research, v. 15, n. 4, gmr15048863, Oct. 2016.| Biblioteca(s): Embrapa Florestas. |
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| Registros recuperados : 90 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
14/07/2017 |
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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 |
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Autoria: |
SANTOS, V. S.; MARTINS FILHO, S.; RESENDE, M. D. V. de; AZEVEDO, C. F.; LOPES, P. S.; GUIMARÃES, S. E. F.; SILVA, F. F. |
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Afiliação: |
V. S. SANTOS, Departamento de Estatística, Universidade Federal de Viçosa; S. MARTINS FILHO, Departamento de Estatística, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; C. F. AZEVEDO, Departamento de Estatística, Universidade Federal de Viçosa; P. S. LOPES, Departamento de Zootecnia, Universidade Federal de Viçosa; S. E. F. GUIMARÃES, Departamento de Zootecnia, Universidade Federal de Viçosa; F. F. SILVA, Departamento de Zootecnia, Universidade Federal de Viçosa. |
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Título: |
Genomic prediction for additive and dominance effects of censored traits in pigs. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 15, n. 4, gmr15048764, Oct. 2016. |
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DOI: |
http://dx.doi.org/10.4238/gmr15048764 |
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Idioma: |
Inglês |
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Conteúdo: |
Age at the time of slaughter is a commonly used trait in animal breeding programs. Since studying this trait involves incomplete observations (censoring), analysis can be performed using survival models or modified linear models, for example, by sampling censored data from truncated normal distributions. For genomic selection, the greatest genetic gains can be achieved by including non-additive genetic effects like dominance. Thus, censored traits with effects on both survival models have not yet been studied under a genomic selection approach. We aimed to predict genomic values using the Cox model with dominance effects and compare these results with the linear model with and without censoring. Linear models were fitted via the maximum likelihood method. For censored data, sampling through the truncated normal distribution was used, and the model was called the truncated normal linear via Gibbs sampling (TNL). We used an F2 pig population; the response variable was time (days) from birth to slaughter. Data were previously adjusted for fixed effects of sex and contemporary group. The model predictive ability was calculated based on correlation of predicted genomic values with adjusted phenotypic values. The results showed that both with and without censoring, there was high agreement between Cox and linear models in selection of individuals and markers. Despite including the dominance effect, there was no increase in predictive ability. This study showed, for the first time, the possibility of performing genomic prediction of traits with censored records while using the Cox survival model with additive and dominance effects. MenosAge at the time of slaughter is a commonly used trait in animal breeding programs. Since studying this trait involves incomplete observations (censoring), analysis can be performed using survival models or modified linear models, for example, by sampling censored data from truncated normal distributions. For genomic selection, the greatest genetic gains can be achieved by including non-additive genetic effects like dominance. Thus, censored traits with effects on both survival models have not yet been studied under a genomic selection approach. We aimed to predict genomic values using the Cox model with dominance effects and compare these results with the linear model with and without censoring. Linear models were fitted via the maximum likelihood method. For censored data, sampling through the truncated normal distribution was used, and the model was called the truncated normal linear via Gibbs sampling (TNL). We used an F2 pig population; the response variable was time (days) from birth to slaughter. Data were previously adjusted for fixed effects of sex and contemporary group. The model predictive ability was calculated based on correlation of predicted genomic values with adjusted phenotypic values. The results showed that both with and without censoring, there was high agreement between Cox and linear models in selection of individuals and markers. Despite including the dominance effect, there was no increase in predictive ability. This study showed, for the first time,... Mostrar Tudo |
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Palavras-Chave: |
Censored data; GBLUP; Mixed model; Modelo mixto; Survival models. |
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Thesagro: |
Porco; Suíno. |
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Thesaurus NAL: |
Animal breeding; Swine. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/161816/1/2016-M.Deon-GMR-Genomic.pdf
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Marc: |
LEADER 02545naa a2200313 a 4500
001 2072712
005 2018-01-03
008 2016 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.4238/gmr15048764$2DOI
100 1 $aSANTOS, V. S.
245 $aGenomic prediction for additive and dominance effects of censored traits in pigs.$h[electronic resource]
260 $c2016
520 $aAge at the time of slaughter is a commonly used trait in animal breeding programs. Since studying this trait involves incomplete observations (censoring), analysis can be performed using survival models or modified linear models, for example, by sampling censored data from truncated normal distributions. For genomic selection, the greatest genetic gains can be achieved by including non-additive genetic effects like dominance. Thus, censored traits with effects on both survival models have not yet been studied under a genomic selection approach. We aimed to predict genomic values using the Cox model with dominance effects and compare these results with the linear model with and without censoring. Linear models were fitted via the maximum likelihood method. For censored data, sampling through the truncated normal distribution was used, and the model was called the truncated normal linear via Gibbs sampling (TNL). We used an F2 pig population; the response variable was time (days) from birth to slaughter. Data were previously adjusted for fixed effects of sex and contemporary group. The model predictive ability was calculated based on correlation of predicted genomic values with adjusted phenotypic values. The results showed that both with and without censoring, there was high agreement between Cox and linear models in selection of individuals and markers. Despite including the dominance effect, there was no increase in predictive ability. This study showed, for the first time, the possibility of performing genomic prediction of traits with censored records while using the Cox survival model with additive and dominance effects.
650 $aAnimal breeding
650 $aSwine
650 $aPorco
650 $aSuíno
653 $aCensored data
653 $aGBLUP
653 $aMixed model
653 $aModelo mixto
653 $aSurvival models
700 1 $aMARTINS FILHO, S.
700 1 $aRESENDE, M. D. V. de
700 1 $aAZEVEDO, C. F.
700 1 $aLOPES, P. S.
700 1 $aGUIMARÃES, S. E. F.
700 1 $aSILVA, F. F.
773 $tGenetics and Molecular Research$gv. 15, n. 4, gmr15048764, Oct. 2016.
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