Abstract
In animal breeding and genetics applications, one topic is the evaluation of sexual dimorphism and genetic correlation between sexes. Also, in some cases, variances may be heterogeneous between levels of factors such as breed and herd as well as sex. Researches about these topics need the method for estimating relevant components of (co)variances. The objectives of this study are to derive a computational procedure of the average information algorithm for the restricted maximum likelihood estimation of the relevant (co)variances, and to estimate genetic correlations between sexes on beef carcass traits. In the current computational procedure, a derived expression for the average information matrix is used, whose elements are expressed using the solutions to the mixed model equations in a bivariate mixed linear model with heterogeneous variance and nil covariance of residuals assumed. For the current procedure, replacing the Hessian matrix by the derived average information matrix, a quasi-Newton type procedure is defined for the iterative estimation. Using simulated datasets, computing performance of the current procedure is investigated comparing with the expectation-maximization algorithm, the current procedure is applied to beef carcass traits data to estimate heritability for each sex and genetic correlation between sexes, and then the characteristics of the current procedure is concisely discussed.
