ArticleJournal of translational medicine2026
Translational prioritization of genetically supported candidate targets and pharmacological annotations for chronic lung diseases: a single-cell eQTL-guided multi-cohort study.
Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundChronic 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).
methodsWe 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.
resultsAsthma 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.
conclusionsBy 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.