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Updated: Jul 17, 2026
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Translational prioritization of genetically supported candidate targets and pharmacological annotations for chronic
Ren Li1,2,3, Jiaji Cheng1,2,3, Yaru Liu1,2,3
1Department of Environmental Health, School of Public Health, Shanxi Medical University, 56 Xinjian South Road, Taiyuan, Shanxi, 030001, PR China.
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.
Insights
This study prioritizes genes linked to chronic lung diseases like asthma using genetic data and cell-specific information. It identifies potential drug targets but emphasizes the need for further validation before clinical use.
Area of Science:
- Genomics and Bioinformatics
- Immunology
- Pharmacology
Background:
- Chronic lung diseases present a significant global health challenge.
- Translating genetic discoveries into actionable therapeutic targets for these diseases is complex.
- Prioritizing gene-cell type-disease associations is crucial for advancing research.
Purpose of the Study:
- To prioritize genetically supported gene-cell type-disease associations for asthma, COPD, IPF, and bronchiectasis.
- To identify and characterize pharmacological annotations for prioritized genes.
- To develop a framework for hypothesis generation in respiratory disease research.
Main Methods:
- Integration of immune-cell-specific single-cell cis-eQTL data with two-sample Mendelian randomization and Bayesian colocalization.
- Utilized FinnGen R12 and UK Biobank for cross-cohort validation.
- Developed a tiered framework for ranking gene-cell type-disease associations based on evidence and consistency.
Main Results:
- Asthma showed the most robust signals, with 6 "Tier 1" candidate genes identified (e.g., CD247, FADS1).
- 17 prioritized genes were mapped to pharmacological databases, suggesting potential for drug repurposing.
- Limited peripheral immune signals in IPF and BE may be due to local tissue effects or study limitations.
Conclusions:
- A statistically grounded map of genetically supported candidate genes for chronic respiratory diseases was developed.
- The framework refines genetic loci into cell-contextualized candidates with pharmacological annotations.
- Findings serve as hypothesis-generating evidence requiring independent validation for therapeutic inference.
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