Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks
| Autor: | 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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| Přispěvatelé: | CLAUDIO DE ULHOA MAGNABOSCO, CPAC, MARCOS FERNANDO OLIVEIRA E COSTA, CNPAF, EDUARDO DA COSTA EIFERT, CPAC, MARCELO GONCALVES NARCISO, CNPAF. |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice) Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
| Popis: | 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 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 Made available in DSpace on 2021-05-05T15:30:53Z (GMT). No. of bitstreams: 1 Magnabosco-Improving-genomic-prediction-accuracy-for-meat-tenderness-in.pdf: 678348 bytes, checksum: f0a1a11347b063df2cace6e507513761 (MD5) Previous issue date: 2020 |
| Databáze: | OpenAIRE |
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