Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets

作者：Zhouzhou Dong, Yunlong Ma, Hua Zhou, Linhui Shi, Gongjie Ye, Lei Yang, Panpan Liu, Li Zhou · 发表于：BMC Pulmonary Medicine · 年份：2020 · DOI：10.1186/s12890-020-01303-7 · 被引用次数：29 · 研究领域：Genetic Associations and Epidemiology、Asthma and respiratory diseases、Bioinformatics and Genomic Networks

Abstract Background Severe asthma is a chronic disease contributing to disproportionate disease morbidity and mortality. From the year of 2007, many genome-wide association studies (GWAS) have documented a large number of asthma-associated genetic variants and related genes. Nevertheless, the molecular mechanism of these identified variants involved in asthma or severe asthma risk remains largely unknown. Methods In the current study, we systematically integrated 3 independent expression quantitative trait loci (eQTL) data ( N = 1977) and a large-scale GWAS summary data of moderate-to-severe asthma ( N = 30,810) by using the Sherlock Bayesian analysis to identify whether expression-related variants contribute risk to severe asthma. Furthermore, we performed various bioinformatics analyses, including pathway enrichment analysis, PPI network enrichment analysis, in silico permutation analysis, DEG analysis and co-expression analysis, to prioritize important genes associated with severe asthma. Results In the discovery stage, we identified 1129 significant genes associated with moderate-to-severe asthma by using the Sherlock Bayesian analysis. Two hundred twenty-eight genes were prominently replicated by using MAGMA gene-based analysis. These 228 replicated genes were enriched in 17 biological pathways including antigen processing and presentation (Corrected P = 4.30 × 10 − 6 ), type I diabetes mellitus (Corrected P = 7.09 × 10 − 5 ), and asthma (Corrected P = 1.72 × 10 − 3 ). W...