DisSNPNet: Predicting disease-associated single-nucleotide polymorphisms using linkage disequilibrium, disease similarity, and 1000 Genomes Project datasets with evidence-based validation.

ABSTRACT: Identifying disease-associated single-nucleotide polymorphisms (SNPs) is fundamental to understanding complex disease genetics, yet genome-wide association studies (GWAS) remain costly and data-intensive. Network-based approaches provide a complementary strategy by exploiting linkage disequilibrium (LD) structure- and disease-relatedness to prioritize candidate variants. We present DisSNPNet, a heterogeneous network-based framework that integrates chromosome-specific SNP LD networks derived from 1000 Genomes Project Phase 1 and Phase 3 data, a MeSH-based disease similarity network, and known disease-SNP associations from CAUSALdb. Random walk with restart was applied to rank SNPs for each disease. Predictive performance was evaluated using disease-wise 3-fold cross-validation with AUROC an

SUBMITTER: Le DH 

PROVIDER: S-EPMC12895193 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature