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

Author(s)

Brito Lopes, Fernando ^{[1, 2]} ; Magnabosco, Claudio U. ^[1] ; Passafaro, Tiago L. ^[3] ; Brunes, Ludmilla C. ^[4] ; Costa, Marcos F. O. ^[5] ; Eifert, Eduardo C. ^[1] ; Narciso, Marcelo G. ^[5] ; Rosa, Guilherme J. M. ^{[3, 6]} ; Lobo, Raysildo B. ^[7] ; Baldi, Fernando ^[1]

Affiliation

^[1] Sao Paulo State Univ UNESP, Dept Anim Sci, BR-14884900 Jaboticabal, SP - Brazil

^[2] Embrapa Cerrados, Brasilia, DF - Brazil

^[3] Univ Wisconsin, Dept Anim Sci, Madison, WI - USA

^[4] Fed Univ Goias UFG, Dept Anim Sci, Goiania, Go - Brazil

^[5] Embrapa Rice & Beans, Santo Antonio De Goias - Brazil

^[6] Univ Wisconsin, Dept Biostat & Med Informat, Madison, WI - USA

^[7] Natl Assoc Breeders & Researchers ANCP, Ribeirao Preto - Brazil

• Document type: Journal article
• Source: JOURNAL OF ANIMAL BREEDING AND GENETICS; v. 137, n. 5 FEB 2020.
• Web of Science Citations: 2

Abstract

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 C pi 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 C pi) 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. (AU)

• FAPESP's process: 17/03221-9 - GENOME-WIDE SELECTION AND FEATURE SUBSET SELECTION OF MARKERS BY BAYESIAN NETWORKS IN BEEF CATTLE USING MULTIPLE-TRAIT MODEL
• Grantee: Fernando Brito Lopes
• Support Opportunities: Scholarships in Brazil - Post-Doctorate