Translational prioritization of genetically supported candidate targets and pharmacological annotations for chronic lung diseases: a single-cell eQTL-guided multi-cohort study.

BACKGROUND

Chronic lung diseases impose a massive global burden, yet translating genetic findings into biologically interpretable target hypotheses remains challenging. We aimed to prioritize genetically supported candidate gene-cell type-disease associations and characterize pharmacological annotations for asthma, chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), and bronchiectasis (BE).

METHODS

We integrated immune-cell-specific single-cell cis-eQTL data (14 immune cell subsets) with two-sample cis-Mendelian randomization and Bayesian colocalization. Using FinnGen R12 and UK Biobank as independent outcome cohorts, we developed a cross-cohort tiered framework to rank candidate gene-cell type-disease associations based on MR evidence, colocalization support, and cross-cohort consistency.

RESULTS

Asthma yielded the most robust signals, highlighting 6 "Tier 1" candidates (e.g., CD247, FADS1) with replicated colocalization support across both cohorts. We mapped 17 prioritized druggable genes to existing drug-, compound-, or metabolite-related annotations via DrugBank, DGIdb, and HMDB. These annotations nominate CD247, FADS1, and other loci for mechanistic and pharmacological follow-up, but they should not be interpreted as direct evidence of clinical repurposing readiness. The limited peripheral immune signals observed in IPF and BE suggest that local tissue niches, together with limited power and phenotype heterogeneity, may influence signal detection.

CONCLUSIONS

By bridging single-cell genomics with pharmacological databases, our tiered prioritization framework provides a statistically grounded map of genetically supported candidate genes for chronic respiratory diseases. The results refine broad genetic loci into cell-contextualized candidate genes and pharmacological annotations that require independent, functional, and pharmacological validation before therapeutic inference. These findings should be interpreted as hypothesis-generating prioritization evidence rather than proof of therapeutic efficacy, clinical utility, or drug-repurposing readiness.