Comparison of Univariate and Multivariate Applications of GBLUP and Artificial Neural Network for Genomic Prediction of Growth and Carcass Traits in the Brangus Heifer Population

Data for growth (birth, weaning and yearling weights) and carcass (longissimus muscle area, intramuscular fat percentage and depth of rib fat) traits and 50K SNP marker data to calculate the genomic relationship matrix were collected from 738 Brangus heifers. Univariate and multivariate genomic best linear unbiased prediction models based on the genomic relationship matrix and univariate and multivariate artificial neural networks models with 1 to 10 neurons, as well as the learning algorithms of Bayesian Regularization, Levenberg–Marquardt and Scaled Conjugate Gradient and transfer function combinations of tangent sigmoid–linear and linear–linear in the hidden-output layers, including the inputs from genomic relationship matrix, were created and applied for the analysis of growth and carcass data. Pearson’s correlation coefficients were used to evaluate the predictive performances of univariate and multivariate genomic best linear unbiased prediction and artificial neural networks models. The overall predictive abilities of genomic best linear unbiased prediction and artificial neural network models were low in the univariate and multivariate analysis. However, the predictive performances of models in the univariate analysis were significantly higher than those from models in the multivariate analysis. In the univariate analysis, models with Bayesian Regularization and the tangent sigmoid–linear or linear–linear transfer function combination yielded higher predictive performances than models with learning algorithms and genomic best linear unbiased prediction models. In addition, predictive performances of models with tangent sigmoid–linear transfer functions were better than those with linear–linear transfer functions in the univariate analysis.