Portelli Michael A, Rakkar Kamini, Hu Sile, Guo Yike, Adcock Ian M, Sayers Ian
Centre for Respiratory Research, Translational Medical Sciences, School of Medicine, National Institute for Health Research Nottingham Biomedical Research Centre, Nottingham University Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom.
Data Science Institute, Imperial College London, London, United Kingdom.
Front Allergy. 2021 Oct 18;2:738741. doi: 10.3389/falgy.2021.738741. eCollection 2021.
Asthma affects more than 300 million people globally and is both under diagnosed and under treated. The most recent and largest genome-wide association study investigating moderate to severe asthma to date was carried out in 2019 and identified 25 independent signals. However, as new and in-depth downstream databases become available, the translational analysis of these signals into target genes and pathways is timely. In this study, unique (U-BIOPRED) and publicly available datasets (HaploReg, Open Target Genetics and GTEx) were investigated for the 25 GWAS signals to identify 37 candidate causal genes. Additional traits associated with these signals were identified through PheWAS using the UK Biobank resource, with asthma and eosinophilic traits amongst the strongest associated. Gene expression omnibus dataset examination identified 13 candidate genes with altered expression profiles in the airways and blood of asthmatic subjects, including and . Gene expression analysis through publicly available datasets highlighted lung tissue cell specific expression, with both and genes showing enriched expression in ciliated cells. Gene enrichment pathway and interaction analysis highlighted the dominance of the gene cluster across many immunological diseases including asthma, type I diabetes, and rheumatoid arthritis. Interaction and prediction analyses found and to be key co-localization partners for other genes, predicted that forms co-expression relationships with 13 other genes, including the gene cluster and that and are co-expressed. Drug interaction analysis revealed that 11 of the candidate genes have an interaction with available therapeutics. This study provides significant insight into these GWAS signals in the context of cell expression, function, and disease relationship with the view of informing future research and drug development efforts for moderate-severe asthma.
哮喘在全球影响着超过3亿人，且存在诊断不足和治疗不足的情况。迄今为止，针对中重度哮喘的最新且规模最大的全基因组关联研究于2019年开展，并确定了25个独立信号。然而，随着新的深入下游数据库的出现，将这些信号转化为靶基因和通路的转化分析变得很及时。在本研究中，针对这25个全基因组关联研究信号，对独特的（U-BIOPRED）和公开可用的数据集（HaploReg、开放目标遗传学和GTEx）进行了研究，以识别37个候选因果基因。通过使用英国生物银行资源进行全表型组关联研究（PheWAS），确定了与这些信号相关的其他特征，其中哮喘和嗜酸性粒细胞特征的关联性最强。基因表达综合数据集检查确定了13个在哮喘患者气道和血液中表达谱改变的候选基因，包括……和……。通过公开可用数据集进行的基因表达分析突出了肺组织细胞特异性表达，……和……基因在纤毛细胞中均显示出富集表达。基因富集通路和相互作用分析突出了……基因簇在包括哮喘、I型糖尿病和类风湿性关节炎在内的许多免疫疾病中的主导地位。相互作用和预测分析发现……和……是其他基因的关键共定位伙伴，预测……与包括……基因簇在内的其他13个基因形成共表达关系，且……和……共表达。药物相互作用分析显示，11个候选基因与现有治疗药物存在相互作用。本研究在细胞表达、功能以及疾病关系方面为这些全基因组关联研究信号提供了重要见解，以期为中重度哮喘的未来研究和药物开发工作提供信息。
