Comparative evaluation of SNP-weighted, Bayesian, and machine learning models for genomic prediction in Holstein cattle

In our dataset, BayesR achieves the highest predictive performance, while GBLUP maintained the best balance between accuracy and computational efficiency. Although weight models perform well for some traits, their overall performance remains inferior to that of the traditional Bayesian model. As more causal SNPs for complex traits are identified, the predictive accuracy of weighted models is expected to further improve.