Genetic evaluation models for post-weaning weight gain in a multibreed Angus-Nelore population

The objective of this work was to identify the most suitable model for the genetic evaluation of post-weaning weight gain in a multibreed Angus-Nelore population. Three models were tested using the Bayesian inference method: traditional animal model (M1), multibreed animal model without (M2) and with segregation (M3). The choice of the best model followed the criteria: number of parameters (Np), deviance information criterion (DIC), conditional predictive ordinate (CPO), and deviance based on Bayes factors. Spearman's rank correlations were estimated for the top 10, 20, and 30% sires. M1 presented the highest values for all criteria, except for Np, and the lowest direct heritability estimate of 0.15 +/- 0.01. The heritability estimates for M2 and M3 were higher and similar, being 0.29 +/- 0.02 and 0.27 +/- 0.02, respectively. M3 showed the lowest values for mean deviance, DIC, and CPO, being the best-fitting model among the three tested. Spearman's correlation between the predicted genetic values for the models ranged from 0.69 to 0.99. The multibreed models are the most suitable for the genetic evaluation of multibreed populations, and M3 shows the best fit for the studied population.