Integrative Analysis of GWAS and Single-Cell eQTL Data Identifies Immune Cell-Type-Specific Genetic Signals Co-occurring in Inflammatory Diseases and Lung Cancer Risk

BackgroundNon-small cell lung cancer (NSCLC) frequently co-occurs with chronic inflammatory conditions such as COPD, RA, IPF, psoriasis, and Dermatomyositis. While epidemiological links are established, the genetic basis of this co-occurrence in specific cell types remains poorly resolved. MethodsWe applied a signal-driven colocalization approach to NSCLC GWAS index variants (p < 1 x 10-6), integrating single-cell eQTL (sc-eQTL) data across cell types within {+/-}100 kb windows to identify stable, compact variant sets that jointly explain GWAS and sc-eQTL signals. Loci with variant sets that optimally account for both NSCLC and sc-eQTL signals were evaluated for directional consistency between GWAS and sc-eQTL effects. In a subset of these loci, we performed locus-specific Mendelian randomization to evaluate whether the same set of genetic variants that influence risk of inflammatory comorbidities also show directionally concordant effects on NSCLC risk. ResultsHundreds of genomic loci with robust concordance between NSCLC risk and cell type-specific gene regulation were identified. Directional analysis revealed consistent regulatory effects in specific immune contexts, and Mendelian randomization supported shared genetic liability between multiple inflammatory diseases and NSCLC risk. ConclusionIntegrative analysis of GWAS and single-cell eQTL data enables the identification of shared regulatory architectures linking inflammatory diseases to lung cancer risk. This framework may support the role of immune cell-type-specific regulation in disease etiology and may inform strategies for risk stratification and mechanistic investigation.