Genomic selection refers to selection decisions based on genomic estimated breeding values (GEBV). To calculate GEBV, first a prediction equation based on a large number of DNA markers, such as SNP (Single Nucleotide Polymorphisms) markers, is derived. The effects of these markers are estimated in a reference population in which animals are both phenotyped and genotyped. In subsequent generations, animals can be genotyped for the markers and the effects of the genotypes summed across the whole genome to predict the GEBV. Genomic prediction has been mainly implemented in ‘simple’ scenarios such as dairy cattle, where one single breed is used worldwide. An important prerequisite for high prediction accuracy in genomic prediction is the availability of a large training population, which allows accurate marker effect estimation. This requirement is not fulfilled in case of regional breeds with a limited number of breeding animals. A multi-breed reference population is a potential solution, however, this approach has been problematic and accuracy of GEBV is rapidly lost. Different genetic architecture and high variance of SNPs effects, followed by different allele frequencies between breeds are the main reasons for inefficiency of Multi-breed genomic prediction. At this research/presentation, several SNP selection criteria were compared in order to improve genomic evaluation across breeds. Results indicate that by appropriate marker selection, genomic prediction can be effective in regional breeds as well.
