Improved Annotation of Asthma Gene Variants with Cell Type Deconvolution of Nasal and Lung Expression-Quantitative Trait Loci

Asthma is a genetically complex inflammatory airway disease associated with over 200 Single nucleotide polymorphisms (SNPs). However, the functional effects of many asthma-associated SNPs in lung and airway epithelial samples are unknown. Here, we aimed to conduct expression quantitative trait loci (eQTL) analysis using a meta-analysis of nasal and lung samples. We hypothesize that incorporating cell-type proportions of airway and lung samples enhances eQTL analysis outcomes. Nasal brush (n=792) and lung tissue (n=1087) samples were investigated separately. Initially, a general eQTL analysis identified genetic variants associated with gene expression levels. Estimated cell-type proportions were adjusted based on the Human Lung Cell Atlas. Additionally, the presence of significant interaction effects between asthma-associated SNPs and each cell type proportion was explored and considered evidence for cell-type associated eQTL. In nasal brush and lung parenchyma samples, 44 and 116 asthma-associated SNPs were identified as eQTLs. Adjusting for cell-type proportions revealed eQTLs for an additional 17 genes (e.g., , , and ) and 16 Genes (e.g., , , and ) in nose and lung, respectively. Moreover, we identified eQTLs for 9 SNPs annotated to genes such as , , displayed significant interactions with cell type proportions of Club, Goblet, and alveolar macrophages. Our findings demonstrate increased power for identifying eQTLs among asthma-associated SNPs by considering cell-type proportion of the bulk-RNA-seq data from nasal and lung tissues. Integration of cell-type deconvolution and eQTL analysis enhances our understanding of asthma genetics and cellular mechanisms, uncovering potential therapeutic targets for personalized interventions.