Genetic Parameter Estimation and Accuracy of Selection for Milk Production Traits in Romanian Black and White Cattle: A Bayesian Multi-Trait Animal Model Using MCMCglmm

Abstract

Abstract

Accurate estimation of genetic parameters is a prerequisite for efficient genetic improvement in dairy cattle. In the Romanian Black and White (BNR) breed, the genetic covariance structure among milk production traits has not been simultaneously estimated under a Bayesian multi-trait framework. A Bayesian multi-trait animal model implemented via Markov chain Monte Carlo (MCMC) methods using MCMCglmm was applied to 8207 standard 305-day lactation records from 16,658 animals with pedigree information. Three traits were analyzed simultaneously: milk yield, fat content, and protein content. Heritabilities, genetic, phenotypic, and environmental correlations, and mean accuracy of selection were estimated, with uncertainty quantified through 95% highest posterior density (HPD) intervals. Heritability estimates were h2 = 0.249 (95% HPD: 0.172–0.332) for milk yield, h2 = 0.504 (95% HPD: 0.380–0.607) for fat content, and h2 = 0.383 (95% HPD: 0.284–0.491) for protein content, with compositional traits showing stronger genetic determination than milk yield. Milk yield was negatively correlated with both fat content (rg= −0.496) and protein content (rg = −0.574), while fat and protein contents were positively correlated (rg = 0.643). The results provide a quantitative basis for the BNR national breeding program and confirm that the negative genetic correlations between milk yield and compositional traits should be considered in any selection index targeting simultaneous improvement in production volume and milk quality.