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

作者：Weijie Zheng, Qi Zhang, Jinfeng He, Bo Han, Qin Zhang, Dongxiao Sun · 发表于：BMC Genomics · 年份：2025 · DOI：10.1186/s12864-025-12218-0 · 被引用次数：4 · 研究领域：Genetic and phenotypic traits in livestock、Genetic Mapping and Diversity in Plants and Animals、Genetic Associations and Epidemiology

BACKGROUND: 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. RESULTS: 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. Howe...