Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.contributorUniversity of Wisconsin-Madison-
Autor(es): dc.contributorUniversidade Federal de Goiás (UFG)-
Autor(es): dc.contributorNational Association of Breeders and Researchers (ANCP)-
Autor(es): dc.creatorBrito Lopes, Fernando [UNESP]-
Autor(es): dc.creatorMagnabosco, Cláudio U. [UNESP]-
Autor(es): dc.creatorPassafaro, Tiago L.-
Autor(es): dc.creatorBrunes, Ludmilla C.-
Autor(es): dc.creatorCosta, Marcos F. O.-
Autor(es): dc.creatorEifert, Eduardo C. [UNESP]-
Autor(es): dc.creatorNarciso, Marcelo G.-
Autor(es): dc.creatorRosa, Guilherme J. M.-
Autor(es): dc.creatorLobo, Raysildo B.-
Autor(es): dc.creatorBaldi, Fernando [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:24:39Z-
Data de disponibilização: dc.date.available2022-02-22T00:24:39Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1111/jbg.12468-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/198476-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/198476-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionDepartment of Animal Science São Paulo State University (UNESP)-
Descrição: dc.descriptionEmbrapa Cerrados-
Descrição: dc.descriptionDepartment of Animal Sciences University of Wisconsin-Madison-
Descrição: dc.descriptionDepartment of Animal Science Federal University of Goiás (UFG)-
Descrição: dc.descriptionEmbrapa Rice and Beans Santo Antônio de Goiás-
Descrição: dc.descriptionDepartment of Biostatistics and Medical Informatics University of Wisconsin-Madison-
Descrição: dc.descriptionNational Association of Breeders and Researchers (ANCP)-
Descrição: dc.descriptionDepartment of Animal Science São Paulo State University (UNESP)-
Descrição: dc.descriptionFAPESP: 2017/03221-9-
Formato: dc.format438-448-
Idioma: dc.languageen-
Relação: dc.relationJournal of Animal Breeding and Genetics-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectanimal breeding-
Palavras-chave: dc.subjectBayesian regression models-
Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjectgenomic selection-
Palavras-chave: dc.subjectZebu-
Título: dc.titleImproving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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