PSVI-16 Comparison of univariate and multivariate machine learning applications for genomic predictions of growth and carcass traits from Brangus heifers

Abstract

Data for growth and carcass traits were obtained from 738 Brangus heifers (3/8 Brahman-Bos indicus × 5/8 Angus-Bos taurus) registered with International Brangus Breeders Association. Phenotypes included body weights (BW, WW, and YW) collected at birth, ~205 and 365 d of age for growth traits and yearling ultrasound assessment of longissimus muscle area (LMA), percent intramuscular fat (IMF), and depth of rib fat (FAT) for carcass traits and were used to compare univariate and multivariate artificial neural networks (ANN) models with the learning algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG)) and transfer functions (tangent sigmoid and linear) using 1 to 20 neurons models based on the input from G genomic relationship matrix. Pearson correlation coefficients for evaluating model performances in testing datasets indicated that univariate ANN models resulted in better genomic prediction than the multivariate ANN models regardless of the learning algorithms and transfer functions for carcass traits. However, there was no clear difference between univariate and multivariate ANN models about genomic prediction for growth traits. In multivariate ANN models, the prediction performance of the combination of learning algorithms and transfer functions changed for growth and carcass traits and there were no superior multivariate ANN models with BR, LM and SCG learning algorithms and transfer functions. However, the correlation coefficients from univariate ANN-BR model indicated better genomic prediction than univariate ANN-LM and ANN-SCG models. The application of different transfer functions did not make any significant difference on the genomic prediction performance of ANN models with different learning algorithms. Results of this study suggest the use of univariate ANN models with BR learning algorithm for genomic prediction of growth and carcass traits.