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

ABSTRACT:

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 fivef

SUBMITTER: Zheng W 

PROVIDER: S-EPMC12613767 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature