Estimation of Genetic Parameters and Prediction for Body Weight of Angus Cattle

With the growing global population, the demand for beef is increasing, making the genetic improvement of beef cattle crucial for sustainable production. This study aimed to estimate genetic parameters using different models and predict body weight in Angus cattle to enhance the accuracy of genetic evaluation and support optimal breeding and selection programs. We used the inclusion or exclusion of maternal genetic effects, maternal permanent environmental effects, and the presence or absence of covariance between maternal and direct genetic effects to distinguish between the six animal models. The variance components and genetic parameters of 13,607 weight records from Angus cattle were estimated using the Average Information Restricted Maximum Likelihood (AI-REML) method. The best estimated model was selected based on the Akaike Information Criterion (AIC) and Likelihood Ratio Test (LRT). The results of this study revealed that, in addition to individual genetic effects, maternal genetic effects had a significant impact on unbiased and accurate genetic parameter estimates of body weight in Angus cattle. The total heritability estimated with the best model for body weight at birth (BW0), 3 months (BW3), 6 months (BW6), 12 months (BW12), and 18 months (BW18) was 0.215 ± 0.007, 0.340 ± 0.021, 0.239 ± 0.035, 0.362 ± 0.044, and 0.225 ± 0.048, respectively. The maternal heritability ranges from 0.017~0.438 and significantly affects Angus cattle throughout their growth and development stages, with the effect decreasing with increasing age. Positive correlations were observed between body weights at different months of age, ranging from 0.061 to 0.828. BW6 has a high positive genetic correlation with later age weight, and BW6 is a good predictor of later age weight. Thus, it is possible to optimize breeding programs and accelerate genetic progress by selecting for higher 6-month-old live weights for early Angus selection. In addition, our results emphasize the importance of considering maternal effects in genetic evaluation to improve the efficiency and accuracy of selection programs and thereby contribute to sustainable genetic improvement in beef cattle.