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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Pecuária Sudeste. |
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Data corrente: |
26/05/2017 |
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Data da última atualização: |
14/06/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SOMAVILLA, A. L.; REGITANO, L. C. de A.; ROSA, G. J. M.; MOKRY, F. B.; MUDADU, M. de A.; TIZIOTO, P. C.; OLIVEIRA, P. S. N. de; SOUZA, M. M. de; COUTINHO, L. L.; MUNARI, D. P. |
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Afiliação: |
Adriana Luiza Somavilla, Unesp; LUCIANA CORREIA DE ALMEIDA REGITANO, CPPSE; Guilherme Jordão Magalhães Rosa, University of Wisconsin; Fabiana Barichello Mokry, UFSCar; MAURICIO DE ALVARENGA MUDADU, CNPTIA; Polyana Cristine Tizioto, UFSCar; Priscila Silva Neubern de Oliveira, UFSCar; Marcela Maria de Souza, UFSCar; Luiz Lehmann Coutinho, USP; Danísio Prado Munari, Unesp. |
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Título: |
Genome-enabled prediction of breeding values for feedlot average daily weight gain in nelore cattle. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
G3: Genes, Genomes, Genetics, v. 7, p. 1-17, 2017. |
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DOI: |
https://doi.org/10.1534/g3.117.041442 |
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Idioma: |
Inglês |
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Conteúdo: |
Nelore is the most economically important cattle breed in Brazil, and the use of genetically improved animals has contributed to increase beef production efficiency. The Brazilian beef feedlot industry has grown considerably in the last decade, so the selection of animals with higher growth rates on feedlot has become quite important. Genomic selection could be used to reduce generation intervals and improve the rate of genetic gains. The aim of this study was to evaluate the prediction of genomic estimated breeding values for average daily gain in 718 feedlot-finished Nelore steers. Analyses of three Bayesian model specifications (Bayesian GBLUP, BayesA, and BayesCπ) were performed with four genotype panels (Illumina BovineHD BeadChip, TagSNPs, GeneSeek High and Low-density indicus). Estimates of Pearson correlations, regression coefficients, and mean squared errors were used to assess accuracy and bias of predictions. Overall, the BayesCπ model resulted in less biased predictions. Accuracies ranged from 0.18 to 0.27, which are reasonable values given the heritability estimates (from 0.40 to 0.44) and sample size (568 animals in the training population). Furthermore, results from Bos taurus indicus panels were as informative as those from Illumina BovineHD, indicating that they could be used to implement genomic selection at lower costs. |
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Palavras-Chave: |
Bayesian model; Bos taurus indicus; Feedlot performance; Genomic selection; Growth; Modelos de regressão; Polimorfismo de nucleotídeo único; Regression models. |
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Thesagro: |
Gado nelore. |
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Thesaurus Nal: |
Feedlots; Marker-assisted selection; Models; Single nucleotide polymorphism. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/160256/1/g3.117.041442.full.pdf
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Marc: |
LEADER 02571naa a2200397 a 4500
001 2070097
005 2019-06-14
008 2017 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1534/g3.117.041442$2DOI
100 1 $aSOMAVILLA, A. L.
245 $aGenome-enabled prediction of breeding values for feedlot average daily weight gain in nelore cattle.$h[electronic resource]
260 $c2017
520 $aNelore is the most economically important cattle breed in Brazil, and the use of genetically improved animals has contributed to increase beef production efficiency. The Brazilian beef feedlot industry has grown considerably in the last decade, so the selection of animals with higher growth rates on feedlot has become quite important. Genomic selection could be used to reduce generation intervals and improve the rate of genetic gains. The aim of this study was to evaluate the prediction of genomic estimated breeding values for average daily gain in 718 feedlot-finished Nelore steers. Analyses of three Bayesian model specifications (Bayesian GBLUP, BayesA, and BayesCπ) were performed with four genotype panels (Illumina BovineHD BeadChip, TagSNPs, GeneSeek High and Low-density indicus). Estimates of Pearson correlations, regression coefficients, and mean squared errors were used to assess accuracy and bias of predictions. Overall, the BayesCπ model resulted in less biased predictions. Accuracies ranged from 0.18 to 0.27, which are reasonable values given the heritability estimates (from 0.40 to 0.44) and sample size (568 animals in the training population). Furthermore, results from Bos taurus indicus panels were as informative as those from Illumina BovineHD, indicating that they could be used to implement genomic selection at lower costs.
650 $aFeedlots
650 $aMarker-assisted selection
650 $aModels
650 $aSingle nucleotide polymorphism
650 $aGado nelore
653 $aBayesian model
653 $aBos taurus indicus
653 $aFeedlot performance
653 $aGenomic selection
653 $aGrowth
653 $aModelos de regressão
653 $aPolimorfismo de nucleotídeo único
653 $aRegression models
700 1 $aREGITANO, L. C. de A.
700 1 $aROSA, G. J. M.
700 1 $aMOKRY, F. B.
700 1 $aMUDADU, M. de A.
700 1 $aTIZIOTO, P. C.
700 1 $aOLIVEIRA, P. S. N. de
700 1 $aSOUZA, M. M. de
700 1 $aCOUTINHO, L. L.
700 1 $aMUNARI, D. P.
773 $tG3: Genes, Genomes, Genetics$gv. 7, p. 1-17, 2017.
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Registro original: |
Embrapa Pecuária Sudeste (CPPSE) |
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| Registros recuperados : 1 | |
| 1. |  | SOMAVILLA, A. L.; REGITANO, L. C. de A.; ROSA, G. J. M.; MOKRY, F. B.; MUDADU, M. de A.; TIZIOTO, P. C.; OLIVEIRA, P. S. N. de; SOUZA, M. M. de; COUTINHO, L. L.; MUNARI, D. P. Genome-enabled prediction of breeding values for feedlot average daily weight gain in nelore cattle. G3: Genes, Genomes, Genetics, v. 7, p. 1-17, 2017.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Pecuária Sudeste. |
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| Registros recuperados : 1 | |
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