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

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dc.contributor.authorBrito Lopes, Fernando [UNESP]
dc.contributor.authorMagnabosco, Cláudio U. [UNESP]
dc.contributor.authorPassafaro, Tiago L.
dc.contributor.authorBrunes, Ludmilla C.
dc.contributor.authorCosta, Marcos F. O.
dc.contributor.authorEifert, Eduardo C. [UNESP]
dc.contributor.authorNarciso, Marcelo G.
dc.contributor.authorRosa, Guilherme J. M.
dc.contributor.authorLobo, Raysildo B.
dc.contributor.authorBaldi, Fernando [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
dc.contributor.institutionUniversity of Wisconsin-Madison
dc.contributor.institutionUniversidade Federal de Goiás (UFG)
dc.contributor.institutionNational Association of Breeders and Researchers (ANCP)
dc.date.accessioned2020-12-12T01:13:55Z
dc.date.available2020-12-12T01:13:55Z
dc.date.issued2020-09-01
dc.description.abstractThe 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.en
dc.description.affiliationDepartment of Animal Science São Paulo State University (UNESP)
dc.description.affiliationEmbrapa Cerrados
dc.description.affiliationDepartment of Animal Sciences University of Wisconsin-Madison
dc.description.affiliationDepartment of Animal Science Federal University of Goiás (UFG)
dc.description.affiliationEmbrapa Rice and Beans Santo Antônio de Goiás
dc.description.affiliationDepartment of Biostatistics and Medical Informatics University of Wisconsin-Madison
dc.description.affiliationNational Association of Breeders and Researchers (ANCP)
dc.description.affiliationUnespDepartment of Animal Science São Paulo State University (UNESP)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2017/03221-9
dc.format.extent438-448
dc.identifierhttp://dx.doi.org/10.1111/jbg.12468
dc.identifier.citationJournal of Animal Breeding and Genetics, v. 137, n. 5, p. 438-448, 2020.
dc.identifier.doi10.1111/jbg.12468
dc.identifier.issn1439-0388
dc.identifier.issn0931-2668
dc.identifier.scopus2-s2.0-85078889611
dc.identifier.urihttp://hdl.handle.net/11449/198476
dc.language.isoeng
dc.relation.ispartofJournal of Animal Breeding and Genetics
dc.sourceScopus
dc.subjectanimal breeding
dc.subjectBayesian regression models
dc.subjectdeep learning
dc.subjectgenomic selection
dc.subjectZebu
dc.titleImproving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networksen
dc.typeArtigo
unesp.author.orcid0000-0003-1292-3331[1]
unesp.author.orcid0000-0001-9012-520X[4]

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