Abstract
The genetic architecture underlying traits associated with Type 1 Gaucher disease (GD1) remains insufficiently explored. We integrated genomic structural equation modeling and multiple post-genome-wide association study (GWAS) methodologies to prioritize candidate SNPs associated with GD1-related variation, identifying 15 loci with strong statistical support. Subsequently, diverse transcriptome-wide association approaches were employed to pinpoint susceptibility gene signals strongly correlated with GD1. For selected candidate genes, we explored the potential structural consequences of missense variants using integrated structure prediction, molecular dynamics simulations, and AI-based thermodynamic stability analyses. These analyses suggested that the mutations may alter protein structure and dynamics, with possible consequences for protein stability and biological function. Next, we screened a large set of publicly available traits to identify GD1-related factors and biomarkers with potential relevance. Finally, a summary data-based polygenic risk score (PRS) was utilized to examine risk associations between 22 autosomes and GD1. Collectively, by modeling a GD1-related phenotype without direct prior measurement, this study provides an initial overview of the shared genetic architecture associated with GD1.