2026 American Academy of Neurology Abstract Website

Objective:

To integrate GWAS-identified single nucleotide polymorphisms (SNPs) associated with Parkinson’s disease (PD) and identify convergent molecular pathways with potential biomarker relevance.

Background:

PD is a progressive neurodegenerative disorder characterized by dopaminergic neuronal loss and α-synuclein aggregation. Although numerous PD-associated SNPs have been identified, their functional interpretation remains incomplete, limiting biomarker development and precision diagnostics. We applied systems-level bioinformatics to reveal convergent genetic pathways that may underlie PD susceptibility and clinical heterogeneity.

Design/Methods:

PD-associated SNPs were retrieved from the NHGRI-EBI GWAS Catalog. SNP–gene associations were established as either expression quantitative trait loci (eQTL) data from substantia nigra and cortical tissues or genetics regions within ±100 kb of each variant. Gene Set Enrichment Analysis (GSEA) and high-confidence (interaction score >0.7) protein–protein interaction (PPI) networks from STRING were used to identify hub genes and functional modules associated with biomarker pathways.

Results:

A total of 325 PD-associated SNPs were mapped to 553 protein-coding genes across 64 GWAS studies. PPI analysis revealed a dense network of 214 proteins and 343 edges centered on SNCA and MAPT. Pathway enrichment highlighted autophagy, lipid metabolism, and neuroinflammation. Thirteen subnetworks were detected, with the largest comprising 18 and 14 nodes, respectively. GSEA of our largest subnetwork revealed significant enrichment for neurocognitive disorders, including Lewy body dementia (FDR=1.59×10-6) and Alzheimer’s disease (FDR=6.09×10-5), with shared loci such as APOE and TOMM40. Notably, 76 PD-SNPs exhibited eQTL effects, particularly within noncoding regulatory regions such as LINC02210, MAPK8IP1P2, and DND1P1.

Conclusions:

Our integrative network analysis identified shared molecular pathways between PD and other neurocognitive disorders, potentially indicating a future application in personalized genomics in order to estimate the risk of PD with and without neurocognitive decline. Our analysis identified noncoding genetic regions as the most affected by PD-associated variants, highlighting a potential regulatory role currently unexplored in PD pathology.