Objective:

To integrate GWAS-identified Alzheimer’s disease variants with functional and network-level data to identify convergent molecular pathways underlying disease susceptibility.

Background:

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia worldwide. Although hundreds of AD-associated single nucleotide polymorphisms (SNPs) have been identified through GWAS, most remain statistically associated without clear functional interpretation. We applied systems-level bioinformatics to investigate convergent molecular pathways in late-onset AD that may inform mechanistic understanding and translational strategies.

Design/Methods:

We retrieved AD-associated SNPs from the NHGRI-EBI GWAS Catalog. We established SNP–gene associations using expression quantitative trait loci (eQTL) data from brain tissue within ±100 kb of each variant. We then performed gene set enrichment analysis (GSEA) and protein–protein interaction (PPI) network analysis using high-confidence STRING data (interaction score >0.7) and network topology analysis. 

Results:

We identified 62 AD-associated SNPs from eight GWAS studies encompassing 135,507 cases and 1,110,156 controls across European, Asian, and African cohorts. A total of 122 protein-coding genes were linked to these variants. PPI analysis revealed a dense network of 61 proteins (clustering coefficient = 0.642), with APOE as the most interconnected node (betweenness centrality = 0.35; 17 undirected edges). Clustering identified a core subnetwork of 13 proteins (APOE, TREM2, CD2AP, BIN1, SORL1, CASS4, MS4A6A, APOC2, APOC1, EPHA1, MS4A4E, APOC4, CD33), with CD33 emerging as the subnetwork hub. The network showed significant enrichment for AD-associated gene sets (family history of AD, FDR = 7.53×10^-44; tauopathy, FDR = 1.19×10^-5) with strong interconnections (p = 1×10^-16).

Conclusions: