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

• DOI: 10.1165/rcmb.2024-0251ma
• Publication Date: 2025-01-21T16:03:34Z
• Authors: Zaid W. El-Husseini; Tatiana Karp; Andy Lan; Tessa E. Gillett; Cancan Qi; Dmitry Khalenkow; Thys van der Molen; Chris Brightling; Alberto Papi; Klaus F. Rabe; Salman Siddiqui; Dave Singh; Monica Kraft; Bianca Beghé; Philippe Joubert; Yohan Bossé; Don Sin; Ana H. Cordero; Wim Timens; Corry-Anke Brandsma; Ke Hao; David C Nickle; Judith M. Vonk; Martijn C. Nawijn; Maarten van den B...; Reinoud Gosens; Alen Faiz; Gerard H Koppelman
• Keywords: Cell type; Trait

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 epithelial samples are unknown. Here, we aimed to conduct expression quantitative trait loci (eQTL) analysis using meta-analysis nasal samples. We hypothesize that incorporating cell-type proportions enhances eQTL outcomes. Nasal brush (n=792) tissue (n=1087) were investigated separately. Initially, general identified genetic variants gene levels. Estimated adjusted based on Human Lung Cell Atlas. Additionally, presence significant interaction between each cell type proportion was explored considered evidence for eQTL. In parenchyma samples, 44 116 as eQTLs. Adjusting revealed eQTLs an additional 17 genes (e.g., FCER1G, CD200R1, GABBR2) 16 Genes CYP2C8, SLC9A2, SGCD) nose lung, respectively. Moreover, 9 annotated such VASP, FOXA3, PCDHB12 displayed interactions Club, Goblet, alveolar macrophages. Our findings demonstrate increased power identifying among by considering bulk-RNA-seq data from tissues. Integration deconvolution our understanding asthma genetics cellular mechanisms, uncovering potential therapeutic targets personalized interventions.