Genomic prediction across populations, using pre-selected markers and differential weight models

Genomic prediction (GP) in numerically small breeds is limited due to the requirement for a large reference set. Across breed prediction has not been very successful either. Our objective was to test alternative models for across breed and multi-breed GP in a small Jersey population, utilizing prior information on marker causality. We used data on 596 Jersey bulls from new Zealand and 5503 Holstein bulls from the Netherlands, all of which had deregressed proofs for stature. Two sets of genotype data were used, one containing 357 potential causal markers identified from a multi-breed meta-GWAS on stature (top markers), while the other contained 48,912 markers on the custom 50k chip, excluding the top markers. We used models in which only one GRM (either top markers, 50k, or top plus 50k markers combined) was fitted, and models in which two GRMs (both the top and 50k) were fitted simultaneously, however with different variance components to weight the GRMs differently. Moreover, we estimated the genetic correlation(s) between the breeds (for each GRM) using a multi-trait GP model, which implicitly weights the contribution of one breed’s information to another. Across breed, we observed low accuracies of GP when the 50k markers were fitted alone (0.06) or when the top markers were added to 50k (0.15). Higher accuracy was obtained when only the top markers were fitted (0.21), whereas the highest accuracy was obtained when fitting 50k and top markers simultaneously as two independent GRMs (0.25). Multi-breed prediction outperformed both within and across breed prediction with accuracies ranging from 0.34 to 0.45, with the same trend as in across breed prediction. Based on our results, the best approach for across and multi-breed GP is to fit models that are able to isolate and differentially weight the most important markers for the trait. Keywords: Across breed genomic prediction, marker pre-selection, multi-trait model, sequence data.