El-Husseini Zaid W, Karp Tatiana, Lan Andy, Gillett Tessa E, Qi Cancan, Khalenkow Dmitry, van der Molen Thys, Brightling Chris, Papi Alberto, Rabe Klaus F, Siddiqui Salman, Singh Dave, Kraft Monica, Beghé Bianca, Joubert Philippe, Bossé Yohan, Sin Don, Cordero Ana H, Timens Wim, Brandsma Corry-Anke, Hao Ke, Nickle David C, Vonk Judith M, Nawijn Martijn C, van den Berge Maarten, Gosens Reinoud, Faiz Alen, Koppelman Gerard H
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Groningen Research Institute for Asthma and COPD (GRIAC).
Am J Respir Cell Mol Biol. 2025 Jun;72(6):607-614. doi: 10.1165/rcmb.2024-0251MA.
Asthma is a genetically complex inflammatory airway disease associated with more than 200 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 ( = 792) and lung tissue ( = 1,087) 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. In addition, 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 eQTL. Adjusting for cell type proportions revealed eQTL for an additional 17 genes (e.g., , , and ) and 16 genes (e.g., , , and ) in nose and lung, respectively. Moreover, we identified eQTL for nine SNPs annotated to genes such as , , and displayed significant interactions with cell type proportions of club, goblet, and alveolar macrophages. Our findings demonstrate increased power for identifying eQTL among asthma-associated SNPs by considering cell type proportion of the bulk RNA-sequencing 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.
哮喘是一种遗传复杂的气道炎症性疾病，与200多个单核苷酸多态性（SNP）相关。然而，许多与哮喘相关的SNP在肺和气道上皮样本中的功能作用尚不清楚。在此，我们旨在通过对鼻腔和肺样本的荟萃分析进行表达定量性状基因座（eQTL）分析。我们假设纳入气道和肺样本的细胞类型比例可提高eQTL分析结果。分别对鼻刷样本（n = 792）和肺组织样本（n = 1,087）进行了研究。最初，进行了一项常规的eQTL分析，以确定与基因表达水平相关的遗传变异。根据人类肺细胞图谱对估计的细胞类型比例进行了调整。此外，还探索了与哮喘相关的SNP和每种细胞类型比例之间显著相互作用效应的存在，并将其视为细胞类型相关eQTL的证据。在鼻刷和肺实质样本中，分别有44个和116个与哮喘相关的SNP被鉴定为eQTL。调整细胞类型比例后，在鼻腔和肺中分别又发现了另外17个基因（如……）和16个基因（如……）的eQTL。此外，我们还确定了9个SNP的eQTL，这些SNP注释的基因如……与俱乐部细胞、杯状细胞和肺泡巨噬细胞的细胞类型比例表现出显著的相互作用。我们的研究结果表明，通过考虑来自鼻腔和肺组织的大量RNA测序数据的细胞类型比例，在与哮喘相关的SNP中识别eQTL的能力有所提高。细胞类型反卷积和eQTL分析的整合增强了我们对哮喘遗传学和细胞机制的理解，揭示了个性化干预的潜在治疗靶点。
