Multiple-trait genetic evaluation for one polychotomous trait and several continuous traits with missing data and unequal models

A method for multiple-trait genetic evaluation for categorical and continuous traits was generalized to a polychotomous rather than a binary trait and to several continuous traits rather than one. Any missing data pattern was allowed. Breeding values were estimated based on an animal model with fixed and random effects differing among traits. Equations in location parameters were solved iteratively within each Fisher scoring step. In each round of scoring, new solutions of the residual covariances among the categorical and the continuous traits were computed based on maximum likelihood estimation and used to reevaluate all partial regression coefficients of liability on the continuous traits for each missing data pattern. Simulation was used to assess the estimation of the residual covariances. The other dispersion parameters were treated as known because their estimation has been treated elsewhere and is analogous to restricted maximum likelihood