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| 141. |  | MAGNABOSCO, C. de U.; DIAS, D. S. de O.; FARIA, C. U. de; TONHATI, H.; LOBO, R. B.; LOS REYES, A. de. Estudo genetico quantitativo do perimetro em analise multicarater utilizando dados de campo de bovinos da raca Nelore. In: REUNIAO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 40., 2003, Santa Maria, RS. Otimizando a producao animal: anais. Santa Maria: SBZ: UFSM, 2003. 1 CD-ROM.| Biblioteca(s): Embrapa Cerrados. |
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| 142. | | SENA, J. S. S.; MATOS, A. S.; MARCONDES, C. R.; BEZERRA, L. A. F.; LÔBO, R. B.; RORATO, P. R. N.; CHAVES, L. C. S. Descriptive study and non-genetic effects on production traits of Nellore breed in the Amazon. Livestock Research for Rural Development, v. 26, n. 9, 2014.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 144. | | YOKOO, M. J. I.; WERNECK, J. N.; PEREIRA, M. C.; ALBUQUERQUE, L. G. de; KOURY FILHO, W.; SAINZ, R. D.; LOBO, R. B.; ARAÚJO, F. R. da C. Correlações genéticas entre escores visuais e características de carcaça medidas por ultrassom em bovinos de corte. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 44, n. 2, p. 197-202, fev. 2009 Título em inglês: Genetic correlations between visual scores and carcass traits measured by real-time ultrasound in beef cattle.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 148. | | FARIA, C. U. de; MAGNABOSCO, C. de U.; LOS REYES, A. de; LÔBO, R. B.; BEZERRA, L. A. F.; SAINZ, R. D. Bayesian inference on field data for genetic parameters for some reproctive and related traits of Nellore cattle (Bos indicus). Genetics and Molecular Biology, Ribeirão Preto, v. 30, n. 2 p. 343-348, 2007.| Biblioteca(s): Embrapa Cerrados. |
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| 149. | | FARIA, C. U. de; MAGNABOSCO, C. de U.; LOS REYES, A. de; LÔBO, R. B.; BEZERRA, L. A. F.; SAINZ, R. D. Bayesian inference on field data for genetic parameters for some reproductive and related traits of Nellore cattle ( Bos indicus). Genetics and Molecular Biology, Ribeirão Preto, v. 30, n. 2, p. 343-348, 2007.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 150. | | FARIA, C. U. de; KOURY FILHO, W.; MAGNABOSCO, C. de U.; ALBUQUERQUE, L. G. de; BEZERRA, L. A. F.; LÔBO, R. B. Bayesian inference in genetic parameter estimation of visual scores in Nellore beef-cattle Genetics and Molecular Biology, v. 32, n. 4, p. 753-760, 2009.| Biblioteca(s): Embrapa Cerrados. |
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| 153. | | LÔBO, R. B.; BEZERRA, L. A. F.; OLIVEIRA, H. N.; MAGNABOSCO, C. de U.; ZAMBIANCHI, A. R.; ALBUQUERQUE, L. G.; BERGMANN, J. A. G.; SAINZ, R. D. Avaliação genética de animais jovens, touros e matrizes. Ribeirão Preto: USP-FMRP. Departamento de Genética. GEMAC, 2003. 86 p. il.| Biblioteca(s): Embrapa Pantanal. |
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| 156. | | MAGNABOSCO, C. de U.; MIGUEL JUNIOR, J. C.; LOBO, R. B.; BRUNES, L. C.; QUEIROZ, L. C. R.; LOPES, F. B. AVALIAÇÃO DA TAXA DE PRENHEZ EM PROGRAMA DE IATF USANDO SÊMENS DE DIFERENTES TOUROS DA RAÇA NELORE. In: CONGRESSO BRASILEIRO DE ZOOTECNIA, 29., 2019, Uberaba. Tecnologias que alimentam o mundo: anais... Uberaba: ABZ: Fazu: ABCZ, 2019. Zootec. 5 p.| Biblioteca(s): Embrapa Cerrados. |
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| 158. | | BALDI, F.; FIGUEIREDO, L. G.; OLIVEIRA, H. N. de; MAGNABOSCO, C. de U.; BEZERRA, L. A. F.; FARIA, C. U.; LOBO, R. B. Bioeconomic selection index (EMGTe) for Nellore Brazil breeding program. In: International Conference on Quantitative Genetics, 5., 2016, Madison. Proceedings... Madison , 2016. (ICQG5)| Biblioteca(s): Embrapa Cerrados. |
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| 159. | | FARIA, C. U.; PEREIRA, C. de F.; SILVA, R. P. da; PESSOA, D. D.; MAGNABOSCO, C. de U.; LOBO, R. B. Avaliação do componente genético na expressão fenotípica de características produtivas de bovinos nelore submetidos à prova de desempenho. Ciência Animal Brasileira, Goiânia, v. 18, p. 1-9, e-30528, 2017.| Biblioteca(s): Embrapa Cerrados. |
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| 160. | | VOZZI, P. A.; NOMELINI, J.; FARIA, L. C. DE; MAGNABOSCO, C. de U.; OLIVEIRA, H. N. DE; FARIA, C. U. DE; LÔBO, R. B. Análise da variabilidade genética de animais participantes do program de melhroamento genético da raça Brahman (PMGRB) In: CONGRESSO BRASILEIRO E LATINO-AMERICANO DA RAÇA BRAHMAN, 1.; BRAHMAN SHOW 2009; MOSTRA TÉCNICO-CIENTÍFICA DA RAÇA BRAHMAN, 1., 2009, Rio de Janeiro. Anais... Rio de Janeiro: ACBB, 2009. 1 CD-ROM.| Biblioteca(s): Embrapa Cerrados. |
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| Registros recuperados : 267 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão; Embrapa Cerrados. |
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Data corrente: |
05/05/2021 |
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Data da última atualização: |
14/05/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
LOPES, F. B.; MAGNABOSCO, C. de U.; PASSAFARO, T. L.; BRUNES, L. C.; COSTA, M. F. O. e; EIFERT, E. da C.; NARCISO, M. G.; ROSA, G. J. M.; LOBO, R. B.; BALDI, F. |
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Afiliação: |
CLAUDIO DE ULHOA MAGNABOSCO, CPAC; MARCOS FERNANDO OLIVEIRA E COSTA, CNPAF; EDUARDO DA COSTA EIFERT, CPAC; MARCELO GONCALVES NARCISO, CNPAF. |
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Título: |
Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Journal of Animal Breeding and Genetics, v. 137, n. 5, 2020. |
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Páginas: |
p. 438-448 |
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Idioma: |
Inglês |
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Conteúdo: |
The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle MenosThe goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relative... Mostrar Tudo |
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Palavras-Chave: |
Bayesian regression models; Carne macia; Deep learning; Genomic selection; Maciez da carne. |
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Thesagro: |
Carne; Gado de Corte; Genética Animal; Seleção Genética. |
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Thesaurus NAL: |
Animal breeding; Zebu. |
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Categoria do assunto: |
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L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/223045/1/Magnabosco-Improving-genomic-prediction-accuracy-for-meat-tenderness-in.pdf
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Marc: |
LEADER 02672naa a2200373 a 4500
001 2131678
005 2021-05-14
008 2020 bl uuuu u00u1 u #d
100 1 $aLOPES, F. B.
245 $aImproving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.$h[electronic resource]
260 $c2020
300 $ap. 438-448
520 $aThe goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle
650 $aAnimal breeding
650 $aZebu
650 $aCarne
650 $aGado de Corte
650 $aGenética Animal
650 $aSeleção Genética
653 $aBayesian regression models
653 $aCarne macia
653 $aDeep learning
653 $aGenomic selection
653 $aMaciez da carne
700 1 $aMAGNABOSCO, C. de U.
700 1 $aPASSAFARO, T. L.
700 1 $aBRUNES, L. C.
700 1 $aCOSTA, M. F. O. e
700 1 $aEIFERT, E. da C.
700 1 $aNARCISO, M. G.
700 1 $aROSA, G. J. M.
700 1 $aLOBO, R. B.
700 1 $aBALDI, F.
773 $tJournal of Animal Breeding and Genetics$gv. 137, n. 5, 2020.
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