Accuracy of genomic prediction when combining two related crossbred populations

Charolais bulls are selected for their crossbreed performance when mated to Montbéliard or Holstein dams. To implement genomic prediction, one could build a reference population for each crossbred population independently. An alternative could be to combine both crossbred populations into a single reference population to increase size and accuracy of prediction. The objective of this study was to investigate the accuracy of genomic prediction by combining different crossbred populations. Three scenarios were considered: 1) using 1 crossbred population as reference to predict phenotype of animals from the same crossbred population, 2) combining the 2 crossbred populations into 1 reference to predict phenotype of animals from 1 crossbred population, and 3) using 1 crossbred population as reference to predict phenotype of animals from the other crossbred population. Traits studied were bone thinness, height, and muscular development. Phenotypes and 45,117 SNP genotypes were available for 1,764 Montbéliard × Charolais calves and 447 Holstein × Charolais calves. The population was randomly spilt into 10 subgroups, which were assigned to the validation one by one. To allow fair comparison between scenarios, size of the reference population was kept constant for all scenarios. Breeding values were estimated with BLUP and genomic BLUP. Accuracy of prediction was calculated as the correlation between the EBV and the phenotypic values of the calves in the validation divided by the square root of the heritability. Genomic BLUP showed higher accuracies (between 0.281 and 0.473) than BLUP (between 0.197 and 0.452). Accuracies tended to be highest when prediction was within 1 crossbred population, intermediate when populations were combined into the reference population, and lowest when prediction was across populations. Decrease in accuracy from a prediction within 1 population to a prediction across populations was more pronounced for bone thinness (-27%) and height (-29%) than for muscular development (-14%). Genetic correlation between the 2 crossbred populations was estimated using pedigree relationships. It was 0.70 for bone thinness, 0.80 for height, and 0.99 for muscular development. Genetic correlation indicates the expected gain in accuracy of prediction when combining different populations into 1 reference population. The larger the genetic correlation is, the larger the benefit is to combine populations for genomic prediction.