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Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks
Author(s) -
Brito Lopes Fernando,
Magnabosco Cláudio U.,
Passafaro Tiago L.,
Brunes Ludmilla C.,
Costa Marcos F. O.,
Eifert Eduardo C.,
Narciso Marcelo G.,
Rosa Guilherme J. M.,
Lobo Raysildo B.,
Baldi Fernando
Publication year - 2020
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12468
Subject(s) - tenderness , bayes' theorem , biology , naive bayes classifier , artificial neural network , artificial intelligence , bayesian probability , genetic architecture , statistics , mathematics , genetics , quantitative trait locus , pattern recognition (psychology) , computer science , support vector machine , zoology , gene
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.