The lack of improvement in prediction accuracy in multi-breed genomic evaluation can be due to the (a) inconsistency in allele substitution effects across breeds, (b) the absence of close family relationships between breeds and (c) between-breed differences in linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) and quantitative trait loci that influence a trait across breeds. The objective of this study was to investigate the possibility of improvement in prediction accuracy by accounting for the LD phase differences in a multi-breed reference population. Prediction accuracy was compared in three different scenarios. Scenarios had separate or combined reference population. In the proposed method, when reference population of two breeds were combined, subset of common SNPs between two breeds with high and similar LD phases were identified. Then, A Bayesian Ridge regression model was used to estimated effects for two set of SNPs, SNPs with similar LD phase in both breeds and the remaining SNPs. Results showed that simple pooling of two reference population into a single reference population did not improve prediction accuracy compared to a separate reference population of each breed. However, accounting for LD phase differences in two breeds, improved prediction accuracy compared to separate training and simple pooling of reference populations. This can be appealing for small populations (e.g., beef) that often have relatively small reference population and a combined genetic evaluation is usually performed on multiple breeds.
