Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease

Parkinson's disease (PD) is a complex neurodegenerative disorder with a significant genetic component. While genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with PD, the reliance on large sample sizes and population-level analyses may overlook variants with lower minor allele frequencies or individual-specific relevance. Individualized Bayesian Inference (IBI) offers a promising method to complement GWAS by identifying and prioritizing candidate genetic markers at both the individual and patients-like-me subgroup levels. This study evaluates the application of IBI to PD genetics, using GWAS as a baseline for comparison. We analyzed genetic data from the Fox Insight online study, including 8840 individuals (8585 PD cases and 255 controls). IBI prioritized variants that were not detected or were ranked substantially lower by GWAS, including variants within or near genes with prior PD association. The top 200 IBI SNPs showed stronger predictive performance in ANN models (AUC = 0.79) than the top 200 GWAS SNPs (AUC = 0.72), providing complementary support for the utility of IBI-based prioritization in this cohort. Notably, IBI highlighted variants with lower minor allele frequencies that GWAS did not detect. This study demonstrates the utility of IBI as a complementary tool for prioritizing PD-related candidate variants and genes for further investigation.