Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma

BMC Medical Genomics

Guobing Xu¹  Fang Yu²  Xiuqing Ma³  Yunlong Ma⁴  Peilan Wang⁵ 

[1] Department of Cardiovascular Medicine, Zhongxiang People’s Hospital, 431900, Zhongxiang, Hubei Province, China;Department of Pediatrics, Chinese PLA General Hospital, 100853, Beijing, China;Department of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, 100853, Beijing, China;Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, P. R. China;State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China;Outpatient Department, Chinese PLA General Hospital, 100853, Beijing, China;

关键词: Genetic variants;    GWAS;    Risk genes;    Gene expression;    Asthma;   

DOI  :  10.1186/s12920-020-00768-z

来源: Springer

【 摘 要 】

BackgroundChildhood-onset asthma is highly affected by genetic components. In recent years, many genome-wide association studies (GWAS) have reported a large group of genetic variants and susceptible genes associated with asthma-related phenotypes including childhood-onset asthma. However, the regulatory mechanisms of these genetic variants for childhood-onset asthma susceptibility remain largely unknown.MethodsIn the current investigation, we conducted a two-stage designed Sherlock-based integrative genomics analysis to explore the cis- and/or trans-regulatory effects of genome-wide SNPs on gene expression as well as childhood-onset asthma risk through incorporating a large-scale GWAS data (N = 314,633) and two independent expression quantitative trait loci (eQTL) datasets (N = 1890). Furthermore, we applied various bioinformatics analyses, including MAGMA gene-based analysis, pathway enrichment analysis, drug/disease-based enrichment analysis, computer-based permutation analysis, PPI network analysis, gene co-expression analysis and differential gene expression analysis, to prioritize susceptible genes associated with childhood-onset asthma.ResultsBased on comprehensive genomics analyses, we found 31 genes with multiple eSNPs to be convincing candidates for childhood-onset asthma risk; such as, PSMB9 (cis-rs4148882 and cis-rs2071534) and TAP2 (cis-rs9267798, cis-rs4148882, cis-rs241456, and trans-10,447,456). These 31 genes were functionally interacted with each other in our PPI network analysis. Our pathway enrichment analysis showed that numerous KEGG pathways including antigen processing and presentation, type I diabetes mellitus, and asthma were significantly enriched to involve in childhood-onset asthma risk. The co-expression patterns among 31 genes were remarkably altered according to asthma status, and 25 of 31 genes (25/31 = 80.65%) showed significantly or suggestively differential expression between asthma group and control group.ConclusionsWe provide strong evidence to highlight 31 candidate genes for childhood-onset asthma risk, and offer a new insight into the genetic pathogenesis of childhood-onset asthma.

【 授权许可】

CC BY