Accuracies of direct genomic breeding values for birth and weaning weights of registered Charolais cattle in Mexico

Context: Genomic prediction is now routinely used in many livestock species to rank individuals based on genomic breeding values (GEBV). Aims: This study reports the first assessment aimed to evaluate the accuracy of direct GEBV for birth (BW) and weaning (WW) weights of registered Charolais cattle in Mexico. Methods: The population assessed included 823 animals genotyped with an array of 77 000 single nucleotide polymorphisms. Genomic prediction used genomic best linear unbiased prediction (GBLUP), Bayes C (BC), and single-step Bayesian regression (SSBR) methods in comparison with a pedigree-based BLUP method. Key results: Our results show that the genomic prediction methods provided low and similar accuracies to BLUP. The prediction accuracy of GBLUP and BC were identical at 0.31 for BW and 0.29 for WW, similar to BLUP. Prediction accuracies of SSBR for BW and WW were up to 4% higher than those by BLUP. Conclusions: Genomic prediction is feasible under current conditions, and provides a slight improvement using SSBR. Implications: Some limitations on reference population size and structure were identified and need to be addressed to obtain more accurate predictions in liveweight traits under the prevalent cattle breeding conditions of Mexico.