Abstract Rationale Integrating genome-wide association study (GWAS) data with expression and splicing quantitative trait loci (eQTL and sQTL) datasets can help identify variants that contribute to disease risk through effects on gene expression and alternative splicing. These regulatory associations provide complementary perspectives on how genetic variation shapes molecular phenotypes that underlie complex traits. In this study, we aimed to characterize eQTL and sQTL datasets from human airway epithelial cells (HAEC-185) and to identify pleiotropic QTLs and genes that may contribute to multiple diseases through shared regulatory mechanisms. Methods RNA sequencing of mRNA isolated from basal human airway epithelial cell cultures of 185 subjects was conducted using Illumina technology. Associations between SNPs within 1 Mb of each gene and expression or splicing quantifications generated with Salmon and Leafcutter were analyzed using TensorQTL. Disease-associated variants were obtained from 16 published disease and lung function GWAS studies, and colocalization analysis was carried out with the moloc package. HAEC-185 eQTL and sQTL data were compared to Genotype-Tissue Expression (GTEx) bulk tissue data using the mashr package. Results In the HAEC-185 dataset, we identified 4,282 eQTLs and 6,236 sQTLs (q 0.05). A total of 761 colocalizations were observed between QTLs and GWAS loci, including 137 with hypertension, 124 with chronic kidney disease, 42 with COPD, 30 with asthma, 17 with major depressive disorder, 12 with sleep apnea, 5 with stroke, and one each with idiopathic pulmonary fibrosis, irritable bowel syndrome, and chronic sputum production. The genes most frequently colocalized across multiple diseases were BTN2A1, ANKDD1B, and POC5, which are involved in immune response, signal transduction, and cell division, respectively. Additionally, 1,216 colocalizations were detected between HAEC-185 eQTLs and sQTLs, suggesting that many sQTLs may also modify gene expression. Cross-tissue comparison using mashr revealed that these datasets most closely resembled the GTEx Esophagus Mucosa and Cultured Fibroblast eQTL and sQTL profiles. Conclusions These results suggest that genetic variation in airway epithelial cells may influence disease risk through effects on both gene expression and splicing. The observed colocalizations between HAEC-185 QTLs and multiple GWAS loci indicate that some regulatory mechanisms may be shared across diseases. Genes such as BTN2A1, ANKDD1B, and POC5 may represent examples of loci with broader regulatory effects, highlighting the potential value of integrating cell-type-specific QTL data with GWASes to better understand complex disease biology. This abstract is funded by: 5T32HL007633-40, K01 HL157613, RO1HL166992, RO1HL171213
Geller-McGrath et al. (Fri,) studied this question.