Genomic selection based on random regression test-day model in dairy cattle with respect to different reference populations

Abstract

In this study, we applied random regression test-day model for genomic prediction in the Holstein population in Shandong Province of China with respect to different reference populations, using either 150 k chip genotypes or imputed sequence genotypes. Three different reference populations were considered, i.e., the Shandong (SD) reference population consisting of 1 688 Holstein cows from Shandong Province, the Non-SD reference population consisting of 5 299 Holstein cows from other parts of China, and the combined population of the two. The SD reference resulted in higher prediction accuracy than the Non-SD reference, although the former was much smaller than the latter. The combined reference further increased the accuracy. These results indicate that the accuracy of genomic prediction cross-population within breed is low, even though the reference population is large. Using imputed sequence data may not significantly improve the cross-population prediction ability. However, the inclusion of data from other populations into the reference population can improve the accuracy of genomic selection.