Including genomics in genetic evaluations can effectively increase selection response, especially
for hard to measure, sex limited, and late in life traits. Modelling the increase in accuracy is useful
when designing reference data projects and when breeders choose animals to genotype. Theoretical
equations exist to predict the EBV accuracy of un-phenotyped animals. However, there are anecdotal
reports that the accuracy obtained in practice was often lower than theoretical predictions. This paper
validated an empirical approach to predicting accuracy in Australian Brahman data for nine traits.
The empirical approach required the accuracy of reference and target animals from a standard
pedigree BLUP genetic evaluation and the accuracy of reference animals from a GBLUP genetic
evaluation. Using this information, a series of equations were applied to obtain the predicted GBLUP
accuracy for target animals. Forward cross-validation showed that the empirical predicted GBLUP
was comparable to the actual GBLUP accuracy observed for target animals (accuracy differed
between 0.9% and 3.6%). In contrast, theoretical predictions differed from the observed GBLUP
accuracy between 5.2% and 21.8%. For smaller (<4,000) reference populations, the theoretical
accuracy was closer to the observed GBLUP accuracy, with differences ranging from 5.2% to
11.6%. The theoretical accuracy was overestimated by between 20.7% and 21.8% for larger
reference populations. Empirical estimates of the effective number of chromosome segments (Me) were between 2.0 and 3.9 times that of theoretical Me, with the greatest difference being for the traits with larger reference sizes. This suggests that the theoretical Me is the reason for overestimated theoretical accuracy predictions.