A COMPUTATIONALLY SIMPLE BAYESIAN METHOD FOR ESTIMATION OF HETEROGENEOUS WITHIN-HERD PHENOTYPIC VARIANCES

Heterogeneous variance among subclasses of the data (e.g., herds) is a potential source of bias in livestock genetic evaluation. The BLUP me thod can account for such heterogeneity if variance components within each subclass are known; unfortunately, information within a particula r subclass is generally insufficient to estimate variances or variance components accurately. Procedures based on empirical Bayes methods an d structural models for variance components have been derived but are not yet computationally feasible on a large scale. Therefore, computat ionally simpler approximations based on phenotypic variances have been proposed and utilized, but these lack, so far, theoretical support. I n this study, a Bayesian procedure for combining within- and across-su bclass phenotypic variances is presented, and a numerical example is g iven. This method offers the computational simplicity of other approxi mate procedures based on phenotypic variances, but it also possesses a stronger theoretical justification. In a simulation study involving a n average of approximately 1.6 million observations in 44,000 region-h erd-year-parity subclasses, the Bayesian method compared favorably wit h other currently available methods for combining within- and across-s ubclass phenotypic variances.