A Bayesian Inference of Genetic Parameters for Sexual Dimorphism Using Carcass Trait Data

Differences in some traits between males and females, called sexual dimorphism, are observed among wild and livestock animals. For traits in which variances may be heterogeneous between sexes in some cases, evaluating the relevant genetic parameters, including genetic correlation between sexes, is an important topic requiring estimation of the components of (co)variances. This study developed a Bayesian approach via the Gibbs sampler to estimate the (co)variance components and genetic parameters of sexual dimorphism. As prior distributions, uniform, multivariate normal, two dimensional scaled inverted Wishart and independent scaled inverted chi-square distributions were used for the macro-environmental effects, breeding values, additive genetic (co)variances and residual variances, respectively. This approach was applied to beef carcass trait data, and the estimates of the (co)variance components and genetic parameters (especially the modes of the marginal posterior densities) were generally in agreement with those obtained using the restricted maximum likelihood procedure.