LAUSR

Selection decisions in genomic selection schemes are made based on genomic breeding values (GBV) of candidates. Thus, the accuracy of GBV is a relevant parameter, as it reflects the stability of prediction and the possibility that the GBV might change when more information becomes available. Accuracy of genomic prediction defined as the correlation between GBV and true breeding values (TBV), however, is difficult to assess, considering TBV of the candidates are not available in reality. In previous studies, several methods were proposed to assess the accuracy of GBV including methods using population parameters or parameters inferred from mixed-model equations. In practice, most approaches tended to overestimate the accuracy of genomic prediction. We thus tested approaches used in previous studies in order to assess the magnitude of bias. Analyses were performed based on simulated data under a variety of scenarios mimicking different livestock breeding programmes. Furthermore, we proposed a novel method and tested it both with simulated data and in a real Holstein data set. The new method provided a better prediction for the accuracy of GBV in the simulated scenarios.