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| Registros recuperados : 14 | |
| 3. |  | OLIVEIRA, L. T. de; MENEZES, G. R. de O.; SILVA, F. F. e; OLIVEIRA, H. R. de; VENTURA, H. T. Modelos de limiar em regressão aleatória para avaliação genética da probabilidade de prenhez na raça Sindi. In: SIMPÓSIO BRASILEIRO DE MELHORAMENTO ANIMAL, 12., 2017, Ribeirão Preto, SP. Fenômica e outras ômicas na produção animal: Anais... Sertãozinho: Sociedade de Melhoramento Animal/Instituto de Zootecnia, 2017.| Biblioteca(s): Embrapa Gado de Corte. |
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| 5. | | 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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| 6. | | RAMOS, I. A.; OLIVEIRA, H. R. de; COSTA, L. C. da; REIS, S. A.; ARAÚJO, E. C. da C.; RIBEIRO, L. A. de A.; LIMA, J. T. de. Efeito relaxante de Erythroxylum subrotundum (Erythroxylaceae) em aorta isolada de rato. In: JORNADA DE INICIAÇÃO CIENTÍFICA DA EMBRAPA SEMIÁRIDO, 7.; JORNADA DE INICIAÇÃO CIENTÍFICA DA FACEPE/UNIVASF, 1., 2012, Petrolina. Anais... Petrolina: Embrapa Semiárido, 2012. p. 527-532. 1 CD-ROM. (Embrapa Semiárido. Documentos, 248).| Biblioteca(s): Embrapa Semiárido. |
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| 7. | | ZUCARELI, C.; CASTRO, M. M.; OLIVEIRA, H. R.; BRANCALIÃO, S. R.; RODRIGUES, J. D.; ONO, E. O.; BOARO, C. S. F. Fitorreguladores e germinação de sementes de maracujá doce em condições de laboratório. Scientia Agraria, Curitiba, v. 4, n. 1-2, p.9-14, 2003.| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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| 8. | | 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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| 9. | | PAIVA, J. T.; RESENDE, M. D. V. de; RESENDE, R. T.; OLIVEIRA, H. R.; SILVA, H. T.; CAETANO, G. C.; CALDERANO, A. A.; LOPES, P. S.; VIANA, J. M. S.; SILVA, F. F. A note on transgenerational epigenetics affecting egg quality traits in meat-type quail. British Poultry Science, v. 59, n. 6, p. 624-628, 2018. Short Communication.| Biblioteca(s): Embrapa Florestas. |
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| 10. | | NEGRI, R.; DALTRO, D.; KLUSKA, S.; OTTO, P. I.; MACHADO, M. A.; PANETTO, J. C. do C.; MARTINS, M. F.; OLIVEIRA, H. R. de; COBUCI, J. A.; SILVA, M. V. G. B. Genomic-enhanced breeding values for heat stress tolerance in Girolando cattle in Brazil. Livestock Science, v. 278, 105360, 2023.| Biblioteca(s): Embrapa Gado de Leite. |
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| 11. | | BARILI, L. D.; VALE, N. M. do; SILVA, F. R. e; CARNEIRO, J. E. de S.; OLIVEIRA, H. R. de; VIANELLO, R. P.; VALDISSER, P. A. M. R.; NASCIMENTO, M. Genome prediction accuracy of common bean via Bayesian models. Ciência Rural, v. 48, n. 8, e20170497, 2018.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 12. | | OLIVEIRA, H. R.; SILVA, F. F.; SIQUEIRA, O. H. G. B. D.; SOUZA, N. O.; JUNQUEIRA, V. S.; RESENDE, M. D. V. de; BORQUIS, R. R. A.; RODRIGUES, M. T. Combining different functions to describe milk, fat, and protein yield in goats using Bayesian multiple-trait random regression models. Journal of Animal Science, v. 94 n. 5, p. 1865-1874, May 2016.| Biblioteca(s): Embrapa Florestas. |
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| 13. | | OLIVEIRA, H. R. de; SILVA, F. F. e; SILVA, M. V. G. B.; SIQUEIRA, O. H. G. B. D. de; MACHADO, M. A.; PANETTO, J. C. do C.; GLÓRIA, L. S.; BRITO, L. F. Bayesian Models combining Legendre and B-spline polynomials for genetic analysis of multiple lactations in Gyr cattle. Livestock Science, v. 201, p. 78-84, 2017.| Biblioteca(s): Embrapa Gado de Leite. |
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| 14. | | OLIVEIRA, L. T. de; BONAFÉ, C. M.; SIVA, F. F. e; VENTURA, H. T.; OLIVEIRA, H. R. de; MENEZES, G. R. de O.; RESENDE, M. D. V. de; VIANA, J. M. S. Bayesian random regression threshold models for genetic evaluation of pregnancy probability in Red Sindhi heifers. Livestock Science, v. 202, p. 166-170, August, 2017. Short communication| Biblioteca(s): Embrapa Florestas; Embrapa Gado de Corte. |
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| Registros recuperados : 14 | |
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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. |
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Data corrente: |
07/07/2017 |
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Data da última atualização: |
27/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
OLIVEIRA, H. R. de; SILVA, F. F. e; SILVA, M. V. G. B.; SIQUEIRA, O. H. G. B. D. de; MACHADO, M. A.; PANETTO, J. C. do C.; GLÓRIA, L. S.; BRITO, L. F. |
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Afiliação: |
Hinayah Rojas de Oliveira, UFV/VIÇOSA; Fabyano Fonseca e Silva, UFV/VIÇOSA; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; Otávio Henrique Gomes Barbosa Dias de Siqueira, UFV/VIÇOSA; MARCO ANTONIO MACHADO, CNPGL; JOAO CLAUDIO DO CARMO PANETTO, CNPGL; Leonardo Siqueira Glória, Universidade Estadual Norte Fluminense; Luiz Fernando Brito, University of Guelph. |
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Título: |
Bayesian Models combining Legendre and B-spline polynomials for genetic analysis of multiple lactations in Gyr cattle. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Livestock Science, v. 201, p. 78-84, 2017. |
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Idioma: |
Inglês |
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Conteúdo: |
We aimed with this study to combine Legendre polynomials (LEG) and linear B-splines (BSP) to describe simultaneously the first and second lactation of Gyr dairy cattle under a multiple-trait random regression models (MTRRM) framework. Additionally we proposed the application of self-organizing map to define the classes of residual variances under these models. A total of 26,438 and 23,892 milk yield test-day records were used, respectively, for the first and second lactations of 3253 Gyr cows. Two preliminary MTRRM analyses considering 10 residual classes were performed: the first one was based on LEG for systematic and random effects for both lactations; and the second one was based on BSP. Three classes were defined by using a self-organizing map: from 6 to 35; 36?185 and 186?305 days in milk. After definition of residual variance classes, a total of 16 MTRRM combining LEG and BSP were compared. The MTRRM based on BSP to describe the systematic effects of the first and second lactation, BSP to describe the random effects of the first lactation and LEG to describe the random effects of the second lactation (BSP-BSP-BSP-LEG) outperformed all other models. From the BSP-BSP-BSP-LEG model, heritability estimates for milk yield over time ranged from 0.1107 to 0.2902, and from 0.2036 to 0.3967, for the first and second lactation, respectively. In general, additive genetic correlation estimates between days in milk within each lactation and between lactations had medium magnitude (mean of genetic correlations were 0.6630, 0.6226 and 0.4749 for the first, second and between both lactations, respectively). We concluded that combining different functions under a MTRRM framework is a feasible alternative for genetic modeling of lactation curves in Gyr dairy cattle. MenosWe aimed with this study to combine Legendre polynomials (LEG) and linear B-splines (BSP) to describe simultaneously the first and second lactation of Gyr dairy cattle under a multiple-trait random regression models (MTRRM) framework. Additionally we proposed the application of self-organizing map to define the classes of residual variances under these models. A total of 26,438 and 23,892 milk yield test-day records were used, respectively, for the first and second lactations of 3253 Gyr cows. Two preliminary MTRRM analyses considering 10 residual classes were performed: the first one was based on LEG for systematic and random effects for both lactations; and the second one was based on BSP. Three classes were defined by using a self-organizing map: from 6 to 35; 36?185 and 186?305 days in milk. After definition of residual variance classes, a total of 16 MTRRM combining LEG and BSP were compared. The MTRRM based on BSP to describe the systematic effects of the first and second lactation, BSP to describe the random effects of the first lactation and LEG to describe the random effects of the second lactation (BSP-BSP-BSP-LEG) outperformed all other models. From the BSP-BSP-BSP-LEG model, heritability estimates for milk yield over time ranged from 0.1107 to 0.2902, and from 0.2036 to 0.3967, for the first and second lactation, respectively. In general, additive genetic correlation estimates between days in milk within each lactation and between lactations had medium magnitude ... Mostrar Tudo |
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Palavras-Chave: |
Random regression; Residual variances; Self-organizing map; Test-day records. |
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Thesagro: |
Bos Indicus. |
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Thesaurus NAL: |
heritability. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
|
Marc: |
LEADER 02630naa a2200277 a 4500
001 2072264
005 2023-01-27
008 2017 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, H. R. de
245 $aBayesian Models combining Legendre and B-spline polynomials for genetic analysis of multiple lactations in Gyr cattle.$h[electronic resource]
260 $c2017
520 $aWe aimed with this study to combine Legendre polynomials (LEG) and linear B-splines (BSP) to describe simultaneously the first and second lactation of Gyr dairy cattle under a multiple-trait random regression models (MTRRM) framework. Additionally we proposed the application of self-organizing map to define the classes of residual variances under these models. A total of 26,438 and 23,892 milk yield test-day records were used, respectively, for the first and second lactations of 3253 Gyr cows. Two preliminary MTRRM analyses considering 10 residual classes were performed: the first one was based on LEG for systematic and random effects for both lactations; and the second one was based on BSP. Three classes were defined by using a self-organizing map: from 6 to 35; 36?185 and 186?305 days in milk. After definition of residual variance classes, a total of 16 MTRRM combining LEG and BSP were compared. The MTRRM based on BSP to describe the systematic effects of the first and second lactation, BSP to describe the random effects of the first lactation and LEG to describe the random effects of the second lactation (BSP-BSP-BSP-LEG) outperformed all other models. From the BSP-BSP-BSP-LEG model, heritability estimates for milk yield over time ranged from 0.1107 to 0.2902, and from 0.2036 to 0.3967, for the first and second lactation, respectively. In general, additive genetic correlation estimates between days in milk within each lactation and between lactations had medium magnitude (mean of genetic correlations were 0.6630, 0.6226 and 0.4749 for the first, second and between both lactations, respectively). We concluded that combining different functions under a MTRRM framework is a feasible alternative for genetic modeling of lactation curves in Gyr dairy cattle.
650 $aheritability
650 $aBos Indicus
653 $aRandom regression
653 $aResidual variances
653 $aSelf-organizing map
653 $aTest-day records
700 1 $aSILVA, F. F. e
700 1 $aSILVA, M. V. G. B.
700 1 $aSIQUEIRA, O. H. G. B. D. de
700 1 $aMACHADO, M. A.
700 1 $aPANETTO, J. C. do C.
700 1 $aGLÓRIA, L. S.
700 1 $aBRITO, L. F.
773 $tLivestock Science$gv. 201, p. 78-84, 2017.
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