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| Registros recuperados : 7 | |
| 2. |  | TEIXEIRA, F. R. F.; NASCIMENTO, M.; CECON, P. R.; CRUZ, C. D.; SILVA, F. F. e; NASCIMENTO, A. C. C.; AZEVEDO, C. F.; MARQUES, D. B. D.; SILVA, M. V. G. B.; CARNEIRO, A. P. S.; PAIXAO, D. M. Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models. Genetics and Molecular Research, v. 20, n. 1, gmr18691, 2021.| Biblioteca(s): Embrapa Gado de Leite. |
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| 3. | | CARRARA, E. R.; LOPES, P. S.; REIS, A. C. Z.; SILVA, J. X.; DIAS, L. C. de C. M.; SCHULTZ, E. B.; MARQUES, D. B. D.; SILVA, D. A. da; VERONEZE, R.; ANDRADE, R. G.; PEIXOTO, M. G. C. D. NASA POWER satellite meteorological system is a good tool for obtaining estimates of the temperature-humidity index under Brazilian conditions compared to INMET weather stations data. International Journal of Biometeorology, v. 67, n. 7, p. 1273-1277, 2023.| Biblioteca(s): Embrapa Gado de Leite. |
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| 4. | | TEIXEIRA, S. A.; IBELLI, A. M. G.; CANTAO, M. E.; OLIVEIRA, H. C. de; LEDUR, M. C.; PEIXOTO, J. de O.; MARQUES, D. B. D.; COSTA, K. A.; COUTINHO, L. L.; GUIMARÃES, S. E. F. Sex determination using RNA-sequencing analyses in early prenatal pig development. Genes, v. 10, n. 1010, 2019.| Biblioteca(s): Embrapa Suínos e Aves. |
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| 5. | | OTTO, P. I.; GUIMARÃES, S. E. F.; VERARDO, L. L.; AZEVEDO, A. L. S.; VANDENPLAS, J.; SEVILLANO, C. A.; MARQUES, D. B. D.; PIRES, M. de F. A.; FREITAS, C. de; VERNEQUE, R. da S.; MARTINS, M. F.; PANETTO, J. C. do C.; CARVALHO, W. A.; GOBO, D. O. R.; SILVA, M. V. G. B.; MACHADO, M. A. Genome-wide association studies for heat stress response in Bos taurus × Bos indicus crossbred cattle. Journal of Dairy Science, v. 102, n. 9, p. 8148-8158, 2019.| Biblioteca(s): Embrapa Gado de Leite. |
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| 6. | | GARCIA, I. S.; TEIXEIRA, S. A.; COSTA, K. A.; MARQUES, D. B. D.; RODRIGUES, G. de A.; COSTA, T. C.; GUIMARÃES, J. D.; OTTO, P. I.; SARAIVA, A.; IBELLI, A. M. G.; CANTAO, M. E.; OLIVEIRA, H. C. de; LEDUR, M. C.; PEIXOTO, J. de O.; GUIMARÃES, S. E. F. L-Arginine supplementation of gilts during early gestation modulates energy sensitive pathways in pig conceptuses. Molecular Reproduction and Development, v. 7, p. 19-34, 2020.| Biblioteca(s): Embrapa Suínos e Aves. |
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| 7. | | TEIXEIRA, S. A.; MARQUES, D. B. D.; COSTA, T. C.; OLIVEIRA, H. C.; COSTA, K. A.; CARRARA, E. R.; SILVA, W. da; GUIMARÃES, J. D.; NEVES, M. N.; IBELLI, A. M. G.; CANTAO, M. E.; LEDUR, M. C.; PEIXOTO, J. de O.; GUIMARÃES, S. E. F. Transcription landscape of the early developmental biology in pigs. Animals, v. 11, n. 1443, 2021.| Biblioteca(s): Embrapa Suínos e Aves. |
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| Registros recuperados : 7 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
13/08/2021 |
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Data da última atualização: |
29/12/2021 |
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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: |
TEIXEIRA, F. R. F.; NASCIMENTO, M.; CECON, P. R.; CRUZ, C. D.; SILVA, F. F. e; NASCIMENTO, A. C. C.; AZEVEDO, C. F.; MARQUES, D. B. D.; SILVA, M. V. G. B.; CARNEIRO, A. P. S.; PAIXAO, D. M. |
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Afiliação: |
Universidade Federal do Piauí; Universidade Federal de Viçosa; Universidade Federal de Viçosa; Universidade Federal de Viçosa; Universidade Federal de Viçosa; A.C.C. NASCIMENTO, Universidade Federal de Viçosa; Universidade Federal de Viçosa; D.B.D. MARQUES, Universidade Federal de Viçosa; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; A.P.S. CARNEIRO, Universidade Federal de Viçosa; Universidade de São Paulo. |
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Título: |
Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 20, n. 1, gmr18691, 2021. |
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DOI: |
http://dx.doi.org/10.4238/gmr18691 |
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Idioma: |
Inglês |
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Conteúdo: |
Knowledge of lactation curves in dairy cattle is essential for understanding the animal production in milk production systems. Genomic prediction of lactation curves represents the genetic pattern of milk production of the animals in the herd. In this context, we made genomic predictions of lactation curves through genome-wide selection (GWS) to characterize the genetic pattern of lactation traits in Girolando cattle based on parameters estimated by nonlinear mixed effects (NLME) models. Data of 1,822 milk control records from 226 Girolando animals genotyped for 37,673 single nucleotide polymorphisms were analyzed. Nine NLME models were compared to identify the equation with the best fit. The lactation traits estimated by the best model were submitted to GWS analysis, using the Bayesian LASSO method. Then, based on the genomic estimated breeding values (GEBVs) obtained, genomic predictions of lactation curves were constructed, and the genetic parameters were calculated. Wood's equation showed the best fit among the evaluated models. Heritabilities ranged from 0.09 to 0.29 for the seven lactation variables (initial production, rates of increase and decline, lactation peak, time to peak yield, persistence and total production). The correlations among GEBVs ranged from -0.85 to 0.98. The concordances between the best animals selected according to the selected traits were greater when the correlations between GEBVs for these traits were also high. Consequently, the methodology allowed us to identify the best nonlinear model and to construct the genetic lactation curves of a Girolando cattle population, as well as to assess the differences between animals and the association between lactation variables. MenosKnowledge of lactation curves in dairy cattle is essential for understanding the animal production in milk production systems. Genomic prediction of lactation curves represents the genetic pattern of milk production of the animals in the herd. In this context, we made genomic predictions of lactation curves through genome-wide selection (GWS) to characterize the genetic pattern of lactation traits in Girolando cattle based on parameters estimated by nonlinear mixed effects (NLME) models. Data of 1,822 milk control records from 226 Girolando animals genotyped for 37,673 single nucleotide polymorphisms were analyzed. Nine NLME models were compared to identify the equation with the best fit. The lactation traits estimated by the best model were submitted to GWS analysis, using the Bayesian LASSO method. Then, based on the genomic estimated breeding values (GEBVs) obtained, genomic predictions of lactation curves were constructed, and the genetic parameters were calculated. Wood's equation showed the best fit among the evaluated models. Heritabilities ranged from 0.09 to 0.29 for the seven lactation variables (initial production, rates of increase and decline, lactation peak, time to peak yield, persistence and total production). The correlations among GEBVs ranged from -0.85 to 0.98. The concordances between the best animals selected according to the selected traits were greater when the correlations between GEBVs for these traits were also high. Consequently, the methodology a... Mostrar Tudo |
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Palavras-Chave: |
Previsão genômica. |
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Thesagro: |
Bovino; Curva de Lactação; Gado Leiteiro. |
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Thesaurus NAL: |
Genome; Girolando; Heritability. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/225151/1/Genomic-prediction.pdf
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Marc: |
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024 7 $ahttp://dx.doi.org/10.4238/gmr18691$2DOI
100 1 $aTEIXEIRA, F. R. F.
245 $aGenomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models.$h[electronic resource]
260 $c2021
520 $aKnowledge of lactation curves in dairy cattle is essential for understanding the animal production in milk production systems. Genomic prediction of lactation curves represents the genetic pattern of milk production of the animals in the herd. In this context, we made genomic predictions of lactation curves through genome-wide selection (GWS) to characterize the genetic pattern of lactation traits in Girolando cattle based on parameters estimated by nonlinear mixed effects (NLME) models. Data of 1,822 milk control records from 226 Girolando animals genotyped for 37,673 single nucleotide polymorphisms were analyzed. Nine NLME models were compared to identify the equation with the best fit. The lactation traits estimated by the best model were submitted to GWS analysis, using the Bayesian LASSO method. Then, based on the genomic estimated breeding values (GEBVs) obtained, genomic predictions of lactation curves were constructed, and the genetic parameters were calculated. Wood's equation showed the best fit among the evaluated models. Heritabilities ranged from 0.09 to 0.29 for the seven lactation variables (initial production, rates of increase and decline, lactation peak, time to peak yield, persistence and total production). The correlations among GEBVs ranged from -0.85 to 0.98. The concordances between the best animals selected according to the selected traits were greater when the correlations between GEBVs for these traits were also high. Consequently, the methodology allowed us to identify the best nonlinear model and to construct the genetic lactation curves of a Girolando cattle population, as well as to assess the differences between animals and the association between lactation variables.
650 $aGenome
650 $aGirolando
650 $aHeritability
650 $aBovino
650 $aCurva de Lactação
650 $aGado Leiteiro
653 $aPrevisão genômica
700 1 $aNASCIMENTO, M.
700 1 $aCECON, P. R.
700 1 $aCRUZ, C. D.
700 1 $aSILVA, F. F. e
700 1 $aNASCIMENTO, A. C. C.
700 1 $aAZEVEDO, C. F.
700 1 $aMARQUES, D. B. D.
700 1 $aSILVA, M. V. G. B.
700 1 $aCARNEIRO, A. P. S.
700 1 $aPAIXAO, D. M.
773 $tGenetics and Molecular Research$gv. 20, n. 1, gmr18691, 2021.
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