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

Wei-wen Zheng,Qi Zhang,Jinfeng He,Bo Han,Qin Zhang,Dongxiao Sun

Published 2025 in BMC Genomics

ABSTRACT

Genomic Best Linear Unbiased Prediction (GBLUP) assumes that all SNPs contribute equally to genetic variance, including those with minimal impact, limiting its accuracy. A major challenge in animal breeding is to develop more scientific models or leverage SNP priors to enhance existing prediction frameworks. We analyzed 122,672 SNPs from 16,122 Holstein cattle with estimated breeding values (EBVs) for nine traits. SNP weight from GWAS and BayesBπ analyses were incorporated into a non-linear model to develop the Dynamic Prior Attention Neural Network (DPAnet), and into GBLUP to construct the SNP-weighted GBLUP (WGBLUP). These were benchmarked against GBLUP, four Bayesian methods, support vector regression (SVR), and kernel ridge regression (KRR) using fivefold cross-validation with 5 repetitions. Specifically, DPAnet significantly improved average accuracy for FP, PP, and FL by 3.0%, 1.1%, and 1.1%, respectively, over GBLUP. WGBLUP_BayesBπ outperformed GBLUP across all traits, averaging a 1.1% gain in accuracy, notably 4.9% for FP, while WGBLUP_GWAS improved accuracy by 1.3% but a 9.1% loss in unbiasedness. Overall, Bayesian models achieve the highest average accuracy (0.625 for BayesR). Even the lowest-performing Bayesian model (BayesCπ, 0.622) outperforms WGBLUP_BayesBπ, WGBLUP_GWAS, DPAnet and GBLUP by 0.8%, 0.6%, 2.2%, 1.9%, respectively. For three type traits, hyperparameter-optimized SVR (0.755), KRR (0.743), and DPAnet (0.741) ranked top three. However, all these advanced methods required, on average, more than six times the computational time of GBLUP, limiting their practical scalability. 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.

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