A comparison of genomically enhanced breeding values predicted by different single-step approaches

Abstract Many countries are currently adopting the single-step model for national genetic evaluations of dairy cattle. The two most widely applied statistical formulations of the single-step model are Genomic Best Linear Unbiased Prediction (ssG-BLUP) and Single Nucleotide Polymorphism BLUP (ssSNP-BLUP), with the main difference being the handling of additive genetic covariance between individuals with genotypes. Using solvers available in MiXBLUP software, our study aimed to compare both models in terms of Genomic Enhanced Breeding Value (GEBV) prediction, bull rankings, and computational efficiency (memory consumption and computational time). The results did not show marked differences in the quality of GEBV prediction expressed by the metrics underlying the Interbull validation, except for ssG-BLUP, APY-based solvers with 3,000 core bulls. However, the ranking of the top 50 bulls differed between models, which has implications for the breeding industry and selection, since the top-ranking bulls are typically the most widely used. 39 and 31 of the top 50 bulls were common to all models for stature and foot angle, respectively. Regarding computational time, ssSNP-BLUP and ssG-BLUP with APY solver using 3,000 bulls were the fastest, and ssG-BLUP with GT solver was the slowest. The selection of core individuals for the APY solver was a crucial element that affected the prediction accuracy. GT or SNP-BLUP solvers can circumvent this issue, since no selection of core individuals is required.