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

By: Uddhav Paneru, Nasir Moghaddar, Julius van der Werf

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.

Read the full article in the Journal of Animal Science.