BAYESIAN-ANALYSIS OF CALVING EASE SCORES AND BIRTH WEIGHTS

In a typical two stage procedure, breeding value prediction for calvin g ease in a threshold model is conditioned on estimated genetic and re sidual covariance matrices. These covariance matrices are traditionall y estimated using analytical approximations. A Gibbs sampler for makin g full Bayesian inferences about fixed effects, breeding values, thres holds and genetic and residual covariance matrices to analyze jointly a discrete trait with multiple ordered categories (calving ease scores ) and a continuously Gaussian distributed trait (birth weights) is des cribed. The Gibbs sampler is implemented by drawing from a set of dens ities - (truncated) normal, uniform and inverted Wishart - making impl ementation of Gibbs sampling straightforward. The method should be use ful for estimating genetic parameters based on features of their margi nal posterior densities taking into full account uncertainties in esti mating other parameters. For routine, large-scale estimation of locati on parameters (breeding values), Gibbs sampling is impractical. The jo int posterior mode given the posterior mean estimates of thresholds an d dispersion parameters is suggested. An analysis of simulated calving ease scores and birth weights is described.