Comparison between multiple-trait and random regression models for genetic evaluation of weight traits in Australian meat sheep.

Random regression (RR) models are recommended as an alternative to multiple-trait (MT) models for better capturing the variance-covariance structure over a trajectory and hence more accurate genetic evaluation of traits that are repeatedly measured and genetically change gradually over time. However, a limited number of studies have been done to empirically compare RR over a MT model to determine how much extra benefit could be achieved from one method over another. We compared the prediction accuracy of RR and MT models for growth traits of Australian meat sheep measured from 60 to 525 d, using 102,579 weight records from 24,872 animals. Variance components and estimated breeding values (EBVs) estimated at specific ages were compared and validated with forward prediction. The accuracy of EBVs obtained from the MT model was 0.58, 0.51, 0.54, and 0.56 for weaning, postweaning, yearling, and hogget weight stages, respectively. RR model produced accuracy estimates of 0.56, 0.51, 0.54, and 0.54 for equivalent weight stages. Regression of adjusted phenotype on EBVs was very similar between the MT and the RR models (P > 0.05). Although the RR model did not significantly increase the accuracy of predicting future progeny performance, there are other benefits of the model such as no limit to the number of records per animal, estimation of EBVs for early and late growth, no need for age correction. Therefore, RR can be considered a more flexible method for the genetic evaluation of Australian sheep for early and late growth, and no need for age correction.

Currently, multiple-trait (MT) models are used in large-scale genetic evaluation of growth traits, where body weight traits are defined as separate traits at a finite number of fixed ages. Random regression (RR) models are expected to be superior since they can handle repeated measurements of weight and model these as a function of the actual age of measurement. These two models were compared in predicting breeding values for the body weight of Australian meat sheep. Phenotypic variation and estimated breeding values (EBVs) estimated at specific ages between 60 and 525 d with RR and MT models were compared and EBVs were validated in progeny data. The accuracy of EBVs in forecasting the performance of progeny was not statistically different between the two models. Other benefits of the RR model include the use of multiple records per animal, estimation of EBVs for early and late growth, with no need for age correction. Hence, RR models can be useful for the genetic evaluation of growth traits of sheep in Australia, but they do not necessarily predict breeding values at different ages more accurately than MT models.