Accuracy of genomic predictions for beef eating quality traits

Eating quality is the primary factor influencing consumer purchasing decisions for beef products. Consumer-derived sensory eating quality traits capture the actual eating experience and are directly aligned with consumer expectations. Accurate genomic prediction of these traits can enable selective breeding, allowing for continuous genetic improvement and consistent consumer satisfaction. In this study we used an international dataset from Australia, the USA and Ireland to estimate genetic parameters for five meat eating quality traits and to evaluate the accuracy of genomic estimated breeding values under different cross-validation scenarios. Heritability estimates ranged from 0.19 to 0.31 for Australia, 0.07 to 0.20 for the USA, 0.09 to 0.17 for Ireland, and 0.14 to 0.22 for the combined dataset. Prediction accuracies for all traits were moderate when using the Australia-only reference population but increased to high accuracies when international data were included. This highlights the value of incorporating international data into the reference population, creating a larger and more diverse dataset, and ultimately improving prediction accuracies.