Fig. 6

Potential accuracy of predicting the genomic value of individuals from population A under different models: within-population, single \({\mathbf{GRM}}\) (WPSG), within-population, multiple \({\mathbf{GRM}}\) (WPMG), multi-population, single \({\mathbf{GRM}}\) (MPSG), multi-population, multiple \({\mathbf{GRM}}\) (MPMG), in relation to different values of genetic correlation (\(\varvec{r}_{\varvec{g}}\)) between population \(\varvec{A}\) and \(\varvec{B}\). The following assumptions were made: \(M_{e}\) within population A based on 500 pre-selected causal SNPs (calculated from real genotype data) = 159; \(M_{e}\) across populations \(A\) and \(B\) based on 500 pre-selected causal SNPs (calculated from real genotype data) = 463; \(M_{e}\) within population \(A\) based on 48,412 non- causal SNPs (calculated from real genotype data) = 280; \(M_{e}\) across populations \(A\) and \(B\) based on 48,412 non-causal SNPs (calculated from real genotype data) = 32,970; \(M_{e}\) within population \(A\) based on all 48,912 SNPs (calculated from real genotype data) = 280; \(M_{e}\) across populations \(A\) and \(B\) based on all 48,912 SNPs (calculated from real genotype data) = 33,242; heritability of the trait = 0.3 in both populations; proportion of genetic variance explained by all SNPs = 0.8; proportion of genetic variance explained by 500 pre-selected causal SNPs = 0.4; number of individuals from population \(A\) in the training population = 476; number of individuals from population \(B\) in the training population = 5553