哮喘是一种异质性炎症性疾病，通常与其他复杂表型相关联。确定与哮喘相关的疾病并揭示介导其相互作用的分子机制可以帮助解开哮喘的异质性。网络分析是解开此类疾病间关系的有力方法。在这里，我们将共同的单核苷酸多态性（SNP）与基因表达之间的物理接触信息和肺部和全血中的表达定量性状基因座（eQTL）数据整合起来，构建了两个组织特异性的空间基因调控网络（GRN）。然后，通过识别受哮喘相关空间eQTL函数影响的基因来定位每个组织特异性GRN内的哮喘GRN（级别0）。根据与哮喘GRN相距四个边缘或级别以内的关联蛋白进行后续识别。然后将0-4级的基因局部调控的eQTL与GWAS Catalog进行查询，以识别在每个级别上富集（超几何检验；FDR ≤ 0.05）的特征。我们在肺部和血液GRN中分别找到了80个和82个显著富集的特征。 所有识别出的特征都已被报道与哮喘共病或相关（正或负）（如抑郁症状和肺癌），除了8个特征，其与哮喘的关联有待确认（如网织红细胞计数）。我们的分析还准确指出了将哮喘与已识别的哮喘相关特征联系起来的变异体和基因，其中一部分在使用新西兰26,781例哮喘患者的健康记录进行共病分析时得到了复制。我们的发现方法在感兴趣的组织中，无需事先选择相互作用特征，可以识别哮喘临近的调控空间中富集的特征。其预测拓宽了我们对哮喘可能的共享分子相互作用和治疗靶点的理解，而目前尚无治愈方法。版权所有 © 2023 Zaied, Fadason和O'Sullivan。

Asthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships.Here, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0-4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level.We identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand.Our discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available.Copyright © 2023 Zaied, Fadason and O’Sullivan.