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Registro Completo |
Biblioteca(s): |
Embrapa Arroz e Feijão; Embrapa Cerrados. |
Data corrente: |
05/05/2021 |
Data da última atualização: |
14/05/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
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. |
Afiliação: |
CLAUDIO DE ULHOA MAGNABOSCO, CPAC; MARCOS FERNANDO OLIVEIRA E COSTA, CNPAF; EDUARDO DA COSTA EIFERT, CPAC; MARCELO GONCALVES NARCISO, CNPAF. |
Título: |
Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Journal of Animal Breeding and Genetics, v. 137, n. 5, 2020. |
Páginas: |
p. 438-448 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Bayesian regression models; Carne macia; Deep learning; Genomic selection; Maciez da carne. |
Thesagro: |
Carne; Gado de Corte; Genética Animal; Seleção Genética. |
Thesaurus Nal: |
Animal breeding; Zebu. |
Categoria do assunto: |
-- L Ciência Animal e Produtos de Origem Animal |
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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Registro original: |
Embrapa Cerrados (CPAC) |
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Registro Completo
Biblioteca(s): |
Embrapa Suínos e Aves. |
Data corrente: |
22/12/2016 |
Data da última atualização: |
22/12/2016 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
MARCIANO, C. M. M.; SAVOLDI, I.; CARMO, K. B. do; IBELLI, A. M. G.; PEIXOTO, J. de O.; LEDUR, M. C. |
Afiliação: |
CAROLINE M. MARINHO MARCIANO, UNC / CNPq; IGOR RICARDO SAVOLDI, UNC / CNPq; KAMILLA BLEIL DO CARMO, UNC / CNPq; ADRIANA MERCIA GUARATINI IBELLI, CNPSA; JANE DE OLIVEIRA PEIXOTO, CNPSA; MONICA CORREA LEDUR, CNPSA. |
Título: |
Expressão do gene Plin1 aos 35 dias em frangos de corte normais e afetados pela necrose da cabeça do fêmur. |
Ano de publicação: |
2016 |
Fonte/Imprenta: |
In: JORNADA DE INICIAÇÃO CIENTÍFICA (JINC), 10., 2016, Concórdia. Anais... Brasília: Embrapa, 2016. p. 111-112. |
Idioma: |
Português |
Palavras-Chave: |
Expressão gênica; Perilipina; Problemas locomotores. |
Thesagro: |
Avicultura. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/152206/1/final8343.pdf
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Marc: |
LEADER 00729nam a2200205 a 4500 001 2059167 005 2016-12-22 008 2016 bl uuuu u00u1 u #d 100 1 $aMARCIANO, C. M. M. 245 $aExpressão do gene Plin1 aos 35 dias em frangos de corte normais e afetados pela necrose da cabeça do fêmur.$h[electronic resource] 260 $aIn: JORNADA DE INICIAÇÃO CIENTÍFICA (JINC), 10., 2016, Concórdia. Anais... Brasília: Embrapa, 2016. p. 111-112.$c2016 650 $aAvicultura 653 $aExpressão gênica 653 $aPerilipina 653 $aProblemas locomotores 700 1 $aSAVOLDI, I. 700 1 $aCARMO, K. B. do 700 1 $aIBELLI, A. M. G. 700 1 $aPEIXOTO, J. de O. 700 1 $aLEDUR, M. C.
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