Comparison of GBLUP and WGBLUP in genomic selection for beef cattle under different LD patterns and mixed multi-population scenarios: a simulation study

Genomic selection (GS) is a key tool to enhance genetic progress in livestock and poultry breeding, but the effects of differences in linkage disequilibrium (LD) patterns between populations and the way reference populations are constructed on the accuracy of genomic prediction (GP) still need to be thoroughly investigated. Therefore, in this study, three beef cattle populations with different genetic backgrounds and LD patterns were simulated to compare the performance of GBLUP, WGBLUP, and their mixed reference population derivation methods (GBLUPmix and WGBLUPmix) in predicting genomic breeding values, with a focus on analyzing the effects of different LD patterns and reference population sizes on prediction performance. The results showed that increasing the reference population size significantly improved the accuracy of GP, especially when the reference population was increased from 4000 to 5000, and the WGBLUPmix model showed the highest prediction accuracy in multi-population integration scenarios. In addition, the performance of the WGBLUPmix model was significantly different in different LD modes. The superiority may be due to the realization of capturing different LD patterns in the genome, adaptation to different genetic contexts of population structure, and better capture of large effect SNP contributions through weight adjustment or assignment. These results highlight the importance of using the WGBLUPmix model to flexibly adjust weights to accommodate different reference population sizes and LD patterns in multi-population genome prediction, demonstrate the superiority of WGBLUP in low-LD populations and its synergistic effect with mixed reference populations, and provide an effective strategy to improve prediction accuracy.