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| 5. |  | ARBEX, W. A.; SILVA, F. F. e; SILVA, M. V. G. B.; BORGES, C. C. H.; OLIVEIRA, F. C. de; VARONA, L.; VERNEQUE, R. da S. Decision Support in Attribute Selection with Machine Learning Approach. In: CONFERENCIA IBÉRICA DE SISTEMAS Y TECNOLOGÍAS DE INFORMACION, 9., 2014, Barcelona. Actas... Barcelona: Aisti; Salle, 2014. CISTI 2014| Biblioteca(s): Embrapa Gado de Leite. |
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| 6. | | MARIGUELE, K. H.; RESENDE, M. D. V. de; VIANA, J. M. S.; SILVA, F. F. e; SILVA, P. S. L. de; KNOP, F. de C. Métodos de análise de dados longitudinais para o melhoramento genético da pinha. Pesquisa Agropecuária Brasileira, Brasília, v. 46, n. 12, p. 1657-1664, dez. 2011.| Biblioteca(s): Embrapa Florestas; Embrapa Unidades Centrais. |
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| 7. | | OLIVEIRA, F. C. de; ALMEIDA, F. N.; SILVA, F. F. e; SILVA, M. V. G. B.; BORGES, C. C. H.; ARBEX, W. A. Metodologia para seleção de marcadores com máquina de vetores de suporte com regressão. In: ARBEX, A.; MARTINS, N. F.; MARTINS, M. F. Talking about computing and genomics - TACG vol.1: Modelos e métodos computacionais em Bioinformática. Brasília, DF: Embrapa, 2014. p. 101-126| Biblioteca(s): Embrapa Gado de Leite. |
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| 8. | | 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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| 9. | | RAMOS, P. V. B.; SILVA, F. F. e; SILVA, L. O. C. da; SANTIAGO, G. G.; MENEZES, G. R. de O.; VIANA, J. M. S.; TORRES JUNIOR, R. A. de A.; GONDO, A.; LUIZ F. BRITO. Genomic evaluation for novel stayability traits in Nellore cattle. Reproduction in Domestic Animals, v. 55, n. 3, p. 266-273, March 2020.| Biblioteca(s): Embrapa Gado de Corte. |
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| Registros recuperados : 10 | |
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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 |
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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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