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| Registros recuperados : 290 | |
| 163. |  | SCHMIDT, G. S.; PACKER, I. U.; DUARTE, F. A. de M.; COSTA, C. N. Formacao de Populacoes-Bases de Aves para Corte. III: Avaliacao das Caracteristicas Reprodutivas, Linha Macho. Pesquisa Agropecuaria Brasileira, Brasilia, v.26, n.6, p.785-790, jun.1991| Biblioteca(s): Embrapa Unidades Centrais. |
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| 169. | | KERN, E. L.; COBUCI, J. A.; COSTA, C. N.; BRACCINI NETO, J.; PICCOLI, M. L. Parametros genéticos para diferentes medidas de longevidade em vacas da raça holandesa, utilizando modelos linear e limiar. In: SIMPÓSIO BRASILEIRO DE MELHORAMENTO ANIMAL, 10., 2013, Uberaba. Anais... Viçosa: Sociedade Brasileira de Melhoramento Animal, 2013.| Biblioteca(s): Embrapa Gado de Leite. |
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| 170. | | COSTA, C. N.; COBUCI, J. A.; FREITAS, A. F. de; VALLOTO, A. A.; SOUZA, M. A. Parâmetros genéticos para o escore da contagem de células somáticas da primeira lactação de vacas da raça Holandesa no Brasil. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 47., 2010, Salvador. Empreendedorismo e progresso científico na zootecnia brasileira: anais. Viçosa, MG: Sociedade Brasileira de Zootecnia, 2010.| Biblioteca(s): Embrapa Gado de Leite. |
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| 172. | | COBUCCI, J. A.; COSTA, C. N.; BRACCINI NETO, J.; ALMEIDA, T. P.; VELHO, M. M. S. Parâmetros genéticos para persistência nas três primeiras lactações de vacas holandesas via modelos de regressão aleatória. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA. 45., 2008, Lavras, MG. Anais... Viçosa, MG: Sociedade Brasileira de Zootecnia, 2008.| Biblioteca(s): Embrapa Gado de Leite. |
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| 173. | | COBUCI, J. A.; BIASSUS, I. de O.; COSTA, C. N.; BRACCINI NETO, J.; CARDOSO, L. L. Parâmetros genéticos para produção de leite, gordura e proteína estimados por diferentes modelos de regressão aleatória. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 47., 2010, Salvador. Empreendedorismo e progresso científico na zootecnia brasileira: anais. Viçosa, MG: Sociedade Brasileira de Zootecnia, 2010.| Biblioteca(s): Embrapa Gado de Leite. |
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| 176. | | SILVA, H. T.; COSTA, C. N.; LOPES, P. S.; VERONEZE, R.; SILVA, F. F. e. Parâmetros genéticos para Stayabilityem bovinos da raça holandesa no Brasil. In: SIMPÓSIO BRASILEIRO DE MELHORAMENTO ANIMAL, 14., 2021, Santa Catarina. Passado, presente e futuro: anais. Santa Catarina: Sociedade Brasileira de Melhoramento Animal, 2021.| Biblioteca(s): Embrapa Gado de Leite. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite; Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
06/11/2019 |
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Data da última atualização: |
06/02/2024 |
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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: |
SILVA, A. A.; SILVA, D. A.; SILVA, F. F.; COSTA, C. N.; LOPES, P. S.; CAETANO, A. R.; THOMPSON, G.; CARVALHEIRA, J. |
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Afiliação: |
A. A. SILVA, UFV; D. A. SILVA, UFV; F. F. SILVA, UFV; CLAUDIO NAPOLIS COSTA, CNPGL; P. S. LOPES, UFV; ALEXANDRE RODRIGUES CAETANO, Cenargen; G. THOMPSON, CIBIO-INBIO, UNIVERSITY OF PORTO, PORTUGAL; J. CARVALHEIRA, CIBIO-INBIO, UNIVERSITY OF PORTO, PORTUGAL. |
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Título: |
Autoregressive single-step test-day model for genomic evaluations of Portuguese Holstein cattle. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Journal of Dairy Science, v. 102, n. 7, p. 6330-6339, 2019. |
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DOI: |
10.3168/jds.2018-15191 |
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Idioma: |
Inglês |
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Conteúdo: |
The multiple-lactation autoregressive test-day (AR) model is the adopted model for the national genetic evaluation of dairy cattle in Portugal. Under this model, animals' permanent environment effects are assumed to follow a first-order autoregressive process over the long (auto-correlations between parities) and short (auto-correlations between test-days within lactation) terms. Given the relevance of genomic prediction in dairy cattle, it is essential to include marker information in national genetic evaluations. In this context, we aimed to evaluate the feasibility of applying the single-step genomic (G)BLUP to analyze milk yield using the AR model in Portuguese Holstein cattle. In total, 11,434,294 test-day records from the first 3 lactations collected between 1994 and 2017 and 1,071 genotyped bulls were used in this study. Rank correlations and differences in reliability among bulls were used to compare the performance of the traditional (A-AR) and single-step (H-AR) models. These 2 modeling approaches were also applied to reduced data sets with records truncated after 2012 (deleting daughters of tested bulls) to evaluate the predictive ability of the H-AR. Validation scenarios were proposed, taking into account young and proven bulls. Average EBV reliabilities, empirical reliabilities, and genetic trends predicted from the complete and reduced data sets were used to validate the genomic evaluation. Average EBV reliabilities for H-AR (A-AR) using the complete data set were 0.52 (0.16) and 0.72 (0.62) for genotyped bulls with no daughters and bulls with 1 to 9 daughters, respectively. These results showed an increase in EBV reliabilities of 0.10 to 0.36 when genomic information was included, corresponding to a reduction of up to 43% in prediction error variance. Considering the 3 validation scenarios, the inclusion of genomic information improved the average EBV reliability in the reduced data set, which ranged, on average, from 0.16 to 0.26, indicating an increase in the predictive ability. Similarly, empirical reliability increased by up to 0.08 between validation tests. The H-AR outperformed A-AR in terms of genetic trends when unproven genotyped bulls were included. The results suggest that the single-step GBLUP AR model is feasible and may be applied to national Portuguese genetic evaluations for milk yield. MenosThe multiple-lactation autoregressive test-day (AR) model is the adopted model for the national genetic evaluation of dairy cattle in Portugal. Under this model, animals' permanent environment effects are assumed to follow a first-order autoregressive process over the long (auto-correlations between parities) and short (auto-correlations between test-days within lactation) terms. Given the relevance of genomic prediction in dairy cattle, it is essential to include marker information in national genetic evaluations. In this context, we aimed to evaluate the feasibility of applying the single-step genomic (G)BLUP to analyze milk yield using the AR model in Portuguese Holstein cattle. In total, 11,434,294 test-day records from the first 3 lactations collected between 1994 and 2017 and 1,071 genotyped bulls were used in this study. Rank correlations and differences in reliability among bulls were used to compare the performance of the traditional (A-AR) and single-step (H-AR) models. These 2 modeling approaches were also applied to reduced data sets with records truncated after 2012 (deleting daughters of tested bulls) to evaluate the predictive ability of the H-AR. Validation scenarios were proposed, taking into account young and proven bulls. Average EBV reliabilities, empirical reliabilities, and genetic trends predicted from the complete and reduced data sets were used to validate the genomic evaluation. Average EBV reliabilities for H-AR (A-AR) using the complete data set w... Mostrar Tudo |
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Palavras-Chave: |
Autoregressive test-day model; Holstein cattle; Single-step genomic BLUP. |
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Categoria do assunto: |
-- |
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Marc: |
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245 $aAutoregressive single-step test-day model for genomic evaluations of Portuguese Holstein cattle.$h[electronic resource]
260 $c2019
520 $aThe multiple-lactation autoregressive test-day (AR) model is the adopted model for the national genetic evaluation of dairy cattle in Portugal. Under this model, animals' permanent environment effects are assumed to follow a first-order autoregressive process over the long (auto-correlations between parities) and short (auto-correlations between test-days within lactation) terms. Given the relevance of genomic prediction in dairy cattle, it is essential to include marker information in national genetic evaluations. In this context, we aimed to evaluate the feasibility of applying the single-step genomic (G)BLUP to analyze milk yield using the AR model in Portuguese Holstein cattle. In total, 11,434,294 test-day records from the first 3 lactations collected between 1994 and 2017 and 1,071 genotyped bulls were used in this study. Rank correlations and differences in reliability among bulls were used to compare the performance of the traditional (A-AR) and single-step (H-AR) models. These 2 modeling approaches were also applied to reduced data sets with records truncated after 2012 (deleting daughters of tested bulls) to evaluate the predictive ability of the H-AR. Validation scenarios were proposed, taking into account young and proven bulls. Average EBV reliabilities, empirical reliabilities, and genetic trends predicted from the complete and reduced data sets were used to validate the genomic evaluation. Average EBV reliabilities for H-AR (A-AR) using the complete data set were 0.52 (0.16) and 0.72 (0.62) for genotyped bulls with no daughters and bulls with 1 to 9 daughters, respectively. These results showed an increase in EBV reliabilities of 0.10 to 0.36 when genomic information was included, corresponding to a reduction of up to 43% in prediction error variance. Considering the 3 validation scenarios, the inclusion of genomic information improved the average EBV reliability in the reduced data set, which ranged, on average, from 0.16 to 0.26, indicating an increase in the predictive ability. Similarly, empirical reliability increased by up to 0.08 between validation tests. The H-AR outperformed A-AR in terms of genetic trends when unproven genotyped bulls were included. The results suggest that the single-step GBLUP AR model is feasible and may be applied to national Portuguese genetic evaluations for milk yield.
653 $aAutoregressive test-day model
653 $aHolstein cattle
653 $aSingle-step genomic BLUP
700 1 $aSILVA, D. A.
700 1 $aSILVA, F. F.
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700 1 $aTHOMPSON, G.
700 1 $aCARVALHEIRA, J.
773 $tJournal of Dairy Science$gv. 102, n. 7, p. 6330-6339, 2019.
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