Objective: To propose a novel approach to explore genetic-metabolic interactions in Parkinson’s disease (PD) by integrating the largest genome-wide association studies (GWASs) for PD and metabolomic traits, identifying key metabolites associated with PD.

Background: PD is a complex disorder influenced by genetic and environmental factors. The latest GWAS identified 90 independent susceptibility loci for PD.

Metabolomics is the study of small molecules in biological systems. Several studies revealed metabolic differences between PD patients and healthy controls. However, findings are inconsistent. Metabolomic GWAS revealed genetic influences on metabolism, but their overlap with PD genetics remains unexplored.

Integrating these genetic datasets may clarify metabolic pathways relevant to PD and uncover biomarkers or therapeutic targets.

Method: We integrated the largest available PD GWAS summary statistics (Nalls et al., 2019) [1] and circulating metabolite GWAS summary statistics (Karjalainen et al., 2024) [2] to explore shared genetic variants and their metabolic implications. Harmonization and merging of datasets were performed. We then examined the 90 independent significant PD risk variants and extracted their p-values from the 233 metabolite GWASs. Heatmaps were generated to visualize PD genetic variants associated with metabolites.

Results: Of the 90 PD-GWAS risk variants, 88 were present within the metabolite GWAS summary statistics. Several PD risk variants showed significant associations in the metabolite GWASs, after Bonferroni correction for multiple testing for 88 PD variants and 28 independent factors/principal components which explain the majority of variance in the metabolic traits (Karjalainen et al) [2] (p<2.03×10^-5). These included: HDL-cholesterol (rs823118), apolipoprotein-1 (rs2269906), citrate (rs12951632), glucose (rs11610045), alanine (rs11610045, rs12951632), glutamine (rs11610045), leucine (rs12951632), valine (rs12951632), albumin (rs6658353), and creatinine (rs6854006, rs2280104). Notably, several PD risk variants were linked to multiple metabolites. No significant associations were found in fatty acids.

Conclusion: This study revealed significant overlap between PD risk variants and metabolite-associated variants. Using a genetics-based approach, it highlights metabolites for further investigation, offering an additional strategy to traditional metabolomic studies.

References: 1. Nalls, M.A., et al., Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet
Neurol, 2019. 18(12): p. 1091-1102.
2. Karjalainen, M.K., et al., Genome-wide characterization of circulating metabolic biomarkers. Nature, 2024. 628(8006): p. 130-138.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/mining-metabolomics-gwas-for-key-parkinsons-disease-variants-a-bioinformatics-approach-to-genetic-metabolic-associations/