4.5 Article

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

期刊

BMC PULMONARY MEDICINE
卷 20, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s12890-020-01303-7

关键词

Severe asthma; Genetic variants; Gene expression; Susceptibility genes; GWAS

资金

  1. Zhejiang Medical and Health Science and Technology Plan Project [2019KY612]
  2. China Postdoctoral Science Foundation [2018 M630667]

向作者/读者索取更多资源

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 (CorrectedP = 4.30 x 10(- 6)), type I diabetes mellitus (CorrectedP = 7.09 x 10(- 5)), and asthma (CorrectedP = 1.72 x 10(- 3)). With the use of a series of bioinformatics analyses, we highlighted 11 important genes such asGNGT2,TLR6, andTTC19as authentic risk genes associated with moderate-to-severe/severe asthma. With respect toGNGT2, there were 3 eSNPs of rs17637472 (P-eQTL = 2.98 x 10(- 8)and P-GWAS = 3.40 x 10(- 8)), rs11265180 (P-eQTL = 6.0 x 10(- 6)and P-GWAS = 1.99 x 10(- 3)), and rs1867087 (P-eQTL = 1.0 x 10(- 4)and P-GWAS = 1.84 x 10(- 5)) identified. In addition,GNGT2is significantly expressed in severe asthma compared with mild-moderate asthma (P = 0.045), and Gngt2 shows significantly distinct expression patterns between vehicle and various glucocorticoids (AnovaP = 1.55 x 10(- 6)). Conclusions Our current study provides multiple lines of evidence to support that these 11 identified genes as important candidates implicated in the pathogenesis of severe asthma.

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