Meta-analysis and in-silico functional characterization of the SNCA variant rs356220 in Parkinson’s disease

Abstract

Abstract The progression of Parkinson’s disease (PD) is influenced by genetic factors, particularly the Synuclein-Alpha (SNCA) gene, which encodes the alpha-synuclein (α-syn) protein involved in dopaminergic neuron degeneration. This study aimed to explore the relationship between rs356220 and PD risk and to understand its functional impact through computational analysis. We thoroughly reviewed nine databases regarding the association between this variant and PD risk. Firstly, a meta-analysis of 9 articles, consisting of 10 studies with 11,638 cases and 37,393 controls was conducted, that identified the C allele of rs356220 as a protective factor against PD (Odds Ratio (OR) 0.91, 95% Confidence Interval (CI): 0.88–0.94, P = 3.82E−08)). Subsequently, we characterized the functional impact of this non-coding variant in the pathophysiology of PD. In-silico process flow included transcription factor binding site (TFBS) analysis, pathway enrichment analysis, and protein interaction analysis. The TFBS analysis suggested that the C allele may influence multiple factors, while subsequent Pathway and Protein Network analyses identified proteins that enhance SNCA expression. Our investigation therefore reveals that rs356220 influences the dynamics of the α-syn protein through interactions with BAD, CANX, SLC18A1, and IRF1, potentially advancing the progression of PD. This research emphasizes the need for holistic study approaches to explore the intricacies of complex disorders like PD.