Single-Cell eQTL Mendelian Randomization and Colocalization Analysis Reveals Immune Cell-Specific Genetic Insights into Functional Dyspepsia

Background Functional dyspepsia (FD) is a common gastrointestinal disorder characterized by multiple clinical symptoms, complex pathophysiology, and limited therapeutic agents. Emerging evidence indicates a role for immune dysregulation in the pathogenesis of FD; however, immune cell-specific gene regulatory mechanisms remain unclear. This study investigated the genetic factors specific to different immune cell types in FD to explore promising therapeutic targets. Methods We performed Mendelian randomization (MR) and Bayesian colocalization analyses integrating single-cell expression quantitative trait loci (eQTL) data from 14 immune cell types with genome-wide association study summary statistics for FD. Functional enrichment and protein–protein interaction (PPI) network analyses were utilized to identify key genes. Phenome-wide association studies (PheWASs) and drug signature enrichment prediction assessed the therapeutic potential and pleiotropic effects of the identified genes. Results Thirty-six immune cell type/eGene combinations strictly satisfied both MR and Bayesian colocalization thresholds, revealing cell-specific genes with putative causal links to FD. The PPI network identified nine key genes (HLA-B, HLA-DQA2, HLA-DQB1, HLA-DRB5, HLA-DOA, HLA-DOB, LTA, GTF2H4, and C6orf136) as critical potential genetic drivers across multiple immune cell types. The majority of these eGenes exhibited no significant pleiotropic associations in PheWAS analysis, indicating a lower risk that might affect suitability for therapeutic targeting. Drug repurposing predicted several approved drugs and natural bioactive molecules (e.g., rifaximin, cinnamaldehyde, and ellagic acid) as hypothesis-generating candidates used for FD therapies and dietary supplements. Conclusions This study identified putative immune cell-specific genetic factors with causal associations with FD and predicted several potential therapeutic candidates using computational integration analyses. Validation in future experiments and clinical studies is required to corroborate our findings.