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Registro Completo |
Biblioteca(s): |
Embrapa Gado de Leite. |
Data corrente: |
06/12/2018 |
Data da última atualização: |
10/04/2024 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
MAIA, L. R.; ANDRADE, P. B.; SANTOS, L.; FONSECA, C. E. M.; CARVALHO, I. N. O.; SOUZA, V. C.; LOPES, F. C. F.; MORENZ, M. J. F. |
Afiliação: |
Laryssa Rodrigues Maia, UFRRJ; Priscila Bernardo de Andrade, UFRRJ; Larissa Santos, UFRRJ; Carlos Elysio Moreira da Fonseca, UFRRJ; Isabel das Neves Oiticica de Carvalho, UFRRJ; Vinícius Carneiro de Souza, UFRRJ; FERNANDO CESAR FERRAZ LOPES, CNPGL; MIRTON JOSE FROTA MORENZ, CNPGL. |
Título: |
Contagem de células somáticas no leite de cabras alimentadas com Flemingia Macrophylla. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 55.; CONGRESSO BRASILEIRO DE ZOOTECNIA, 28., 2018, Goiânia. Construindo saberes, formando pessoas e transformando a produção animal: anais eletrônicos. Goiânia: Sociedade Brasileira de Zootecnia, 2018. |
Idioma: |
Português |
Thesagro: |
Forrageira Tropical; Higiene; Leguminosa. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1100882/1/Contagem-de-celulas-somaticas.pdf
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Marc: |
LEADER 00866nam a2200217 a 4500 001 2100882 005 2024-04-10 008 2018 bl uuuu u00u1 u #d 100 1 $aMAIA, L. R. 245 $aContagem de células somáticas no leite de cabras alimentadas com Flemingia Macrophylla.$h[electronic resource] 260 $aIn: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 55.; CONGRESSO BRASILEIRO DE ZOOTECNIA, 28., 2018, Goiânia. Construindo saberes, formando pessoas e transformando a produção animal: anais eletrônicos. Goiânia: Sociedade Brasileira de Zootecnia$c2018 650 $aForrageira Tropical 650 $aHigiene 650 $aLeguminosa 700 1 $aANDRADE, P. B. 700 1 $aSANTOS, L. 700 1 $aFONSECA, C. E. M. 700 1 $aCARVALHO, I. N. O. 700 1 $aSOUZA, V. C. 700 1 $aLOPES, F. C. F. 700 1 $aMORENZ, M. J. F.
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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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Registro Completo
Biblioteca(s): |
Embrapa Gado de Leite. |
Data corrente: |
04/06/2014 |
Data da última atualização: |
06/02/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 4 |
Autoria: |
NEVES, H. H.; CARVALHEIRO, R.; O'BRIEN, A. M.; UTSUNOMIYA, Y. T.; CARMO, A. S. do; SCHENKEL, F. S.; SÖLKNER, J.; MCEWAN, J. C.; VAN TASSELL, C. P.; COLE, J. B.; SILVA, M. V. G. B.; QUEIROZ, S. A.; SONSTEGARD, T. S.; GARCIA, J. F. |
Afiliação: |
Haroldo HR Neves; Roberto Carvalheiro; Ana M Pérez O'Brien; Yuri T Utsunomiya; Adriana S. do Carmo; Flávio S Schenkel; Johann Sölkner; John C McEwan; Curtis P Van Tassell; John B Cole; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; Sandra A Queiroz; Tad S Sonstegard; José Fernando Garcia. |
Título: |
Accuracy of genomic predictions in Bos indicus (Nellore) cattle. |
Ano de publicação: |
2014 |
Fonte/Imprenta: |
Genetics Selection Evolution, v. 46, article 17, 2014. |
DOI: |
https://doi.org/10.1186/1297-9686-46-17 |
Idioma: |
Inglês |
Conteúdo: |
Background- Nellore cattle play an important role in beef production in tropical systems and there is great interest in determining if genomic selection can contribute to accelerate genetic improvement of production and fertility in this breed. We present the first results of the implementation of genomic prediction in a Bos indicus (Nellore) population. Methods - Influential bulls were genotyped with the Illumina Bovine HD chip in order to assess genomic predictive ability for weight and carcass traits, gestation length, scrotal circumference and two selection indices. 685 samples and 320 238 single nucleotide polymorphisms (SNPs) were used in the analyses. A forward-prediction scheme was adopted to predict the genomic breeding values (DGV). In the training step, the estimated breeding values (EBV) of bulls were deregressed (dEBV) and used as pseudo-phenotypes to estimate marker effects using four methods: genomic BLUP with or without a residual polygenic effect (GBLUP20 and GBLUP0, respectively), a mixture model (Bayes C) and Bayesian LASSO (BLASSO). Empirical accuracies of the resulting genomic predictions were assessed based on the correlation between DGV and dEBV for the testing group. Results - Accuracies of genomic predictions ranged from 0.17 (navel at weaning) to 0.74 (finishing precocity). Across traits, Bayesian regression models (Bayes C and BLASSO) were more accurate than GBLUP. The average empirical accuracies were 0.39 (GBLUP0), 0.40 (GBLUP20) and 0.44 (Bayes C and BLASSO). Bayes C and BLASSO tended to produce deflated predictions (i.e. slope of the regression of dEBV on DGV greater than 1). Further analyses suggested that higher-than-expected accuracies were observed for traits for which EBV means differed significantly between two breeding subgroups that were identified in a principal component analysis based on genomic relationships. Conclusions -Bayesian regression models are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions. Recurrent updates of the training population would be required to enable accurate prediction of the genetic merit of young animals. The technical feasibility of applying genomic prediction in a Bos indicus (Nellore) population was demonstrated. Further research is needed to permit cost-effective selection decisions using genomic information. MenosBackground- Nellore cattle play an important role in beef production in tropical systems and there is great interest in determining if genomic selection can contribute to accelerate genetic improvement of production and fertility in this breed. We present the first results of the implementation of genomic prediction in a Bos indicus (Nellore) population. Methods - Influential bulls were genotyped with the Illumina Bovine HD chip in order to assess genomic predictive ability for weight and carcass traits, gestation length, scrotal circumference and two selection indices. 685 samples and 320 238 single nucleotide polymorphisms (SNPs) were used in the analyses. A forward-prediction scheme was adopted to predict the genomic breeding values (DGV). In the training step, the estimated breeding values (EBV) of bulls were deregressed (dEBV) and used as pseudo-phenotypes to estimate marker effects using four methods: genomic BLUP with or without a residual polygenic effect (GBLUP20 and GBLUP0, respectively), a mixture model (Bayes C) and Bayesian LASSO (BLASSO). Empirical accuracies of the resulting genomic predictions were assessed based on the correlation between DGV and dEBV for the testing group. Results - Accuracies of genomic predictions ranged from 0.17 (navel at weaning) to 0.74 (finishing precocity). Across traits, Bayesian regression models (Bayes C and BLASSO) were more accurate than GBLUP. The average empirical accuracies were 0.39 (GBLUP0), 0.40 (GBLUP20) and 0.44 (Bayes ... Mostrar Tudo |
Palavras-Chave: |
Genomic selection; Nellore cattle. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/116427/1/Cnpgl-2014-Genetics-Selection-Evolution-Accuracy-of-genomic.pdf
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Marc: |
LEADER 03329naa a2200313 a 4500 001 1987574 005 2024-02-06 008 2014 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1186/1297-9686-46-17$2DOI 100 1 $aNEVES, H. H. 245 $aAccuracy of genomic predictions in Bos indicus (Nellore) cattle.$h[electronic resource] 260 $c2014 520 $aBackground- Nellore cattle play an important role in beef production in tropical systems and there is great interest in determining if genomic selection can contribute to accelerate genetic improvement of production and fertility in this breed. We present the first results of the implementation of genomic prediction in a Bos indicus (Nellore) population. Methods - Influential bulls were genotyped with the Illumina Bovine HD chip in order to assess genomic predictive ability for weight and carcass traits, gestation length, scrotal circumference and two selection indices. 685 samples and 320 238 single nucleotide polymorphisms (SNPs) were used in the analyses. A forward-prediction scheme was adopted to predict the genomic breeding values (DGV). In the training step, the estimated breeding values (EBV) of bulls were deregressed (dEBV) and used as pseudo-phenotypes to estimate marker effects using four methods: genomic BLUP with or without a residual polygenic effect (GBLUP20 and GBLUP0, respectively), a mixture model (Bayes C) and Bayesian LASSO (BLASSO). Empirical accuracies of the resulting genomic predictions were assessed based on the correlation between DGV and dEBV for the testing group. Results - Accuracies of genomic predictions ranged from 0.17 (navel at weaning) to 0.74 (finishing precocity). Across traits, Bayesian regression models (Bayes C and BLASSO) were more accurate than GBLUP. The average empirical accuracies were 0.39 (GBLUP0), 0.40 (GBLUP20) and 0.44 (Bayes C and BLASSO). Bayes C and BLASSO tended to produce deflated predictions (i.e. slope of the regression of dEBV on DGV greater than 1). Further analyses suggested that higher-than-expected accuracies were observed for traits for which EBV means differed significantly between two breeding subgroups that were identified in a principal component analysis based on genomic relationships. Conclusions -Bayesian regression models are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions. Recurrent updates of the training population would be required to enable accurate prediction of the genetic merit of young animals. The technical feasibility of applying genomic prediction in a Bos indicus (Nellore) population was demonstrated. Further research is needed to permit cost-effective selection decisions using genomic information. 653 $aGenomic selection 653 $aNellore cattle 700 1 $aCARVALHEIRO, R. 700 1 $aO'BRIEN, A. M. 700 1 $aUTSUNOMIYA, Y. T. 700 1 $aCARMO, A. S. do 700 1 $aSCHENKEL, F. S. 700 1 $aSÖLKNER, J. 700 1 $aMCEWAN, J. C. 700 1 $aVAN TASSELL, C. P. 700 1 $aCOLE, J. B. 700 1 $aSILVA, M. V. G. B. 700 1 $aQUEIROZ, S. A. 700 1 $aSONSTEGARD, T. S. 700 1 $aGARCIA, J. F. 773 $tGenetics Selection Evolution$gv. 46, article 17, 2014.
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