A Stochastic Simulation Study on Validation of an Approximate Multitrait Model Using Preadjusted Data for Prediction of Breeding Values

Abstract

Three different models for prediction of breeding values were compared in a stochastic simulation study of a dairy cattle population of 100,000 cows. The simulation was done in 2 steps. The first step involved 15 yr of selection using breeding values obtained in a univariate model for production and a trivariate model for mastitis occurrence, udder depth, and somatic cell score, in which production and mastitis occurrence were included in the breeding goal. This was done to create an initial population that had already been under selection. The second step consisted of 20 replicates of 4 different scenarios set up to make it possible to compare the different models. Two scenarios were based on univariate evaluations and one for udder health traits on trivariate evaluations, with 2 different breeding goals. In another scenario, an approximate multitrait model using preadjusted data in a 2-step procedure was used and in the last scenario, a complete linear multitrait model was carried out. Differences in genetic response in total merit over the last 15 yr of selection were compared and used to rank the models. The linear multitrait model gave the highest regression coefficient of true genetic values on year (3.073±0.069 in economic units), and this was significantly better than for the approximate multitrait model (2.819±0.047), which again was significantly better than for the univariate approach (2.672±0.060). The linear multitrait model cannot be applied to nearly the same number of traits as the approximate model. Therefore, the approximate model with developments handling breeding values from more complex models than presented in this paper is an option of choice in countries providing total merit indices that combine many traits because it does not neglect correlations between these traits.