Research Paper Volume 12, Issue 19 pp 19173—19220
Integrative genomics analysis identifies promising SNPs and genes implicated in tuberculosis risk based on multiple omics datasets
- 1 Department of Infectious Diseases, Shengzhou People’s Hospital, The First Affiliated Hospital of Zhejiang University Shengzhou Branch, Shengshou 312400, Zhejiang, China
- 2 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 310003, Zhejiang, China
- 3 Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou 325027, Zhejiang, China
- 4 School of Biomedical Engineering, School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, Zhejiang, China
Received: April 14, 2020 Accepted: July 7, 2020 Published: October 13, 2020https://doi.org/10.18632/aging.103744
How to Cite
Copyright: © 2020 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
More than 10 GWASs have reported numerous genetic loci associated with tuberculosis (TB). However, the functional effects of genetic variants on TB remains largely unknown. In the present study, by combining a reported GWAS summary dataset (N = 452,264) with 3 independent eQTL datasets (N = 2,242) and other omics datasets downloaded from public databases, we conducted an integrative genomics analysis to highlight SNPs and genes implicated in TB risk. Based on independent biological and technical validations, we prioritized 26 candidate genes with eSNPs significantly associated with gene expression and TB susceptibility simultaneously; such as, CDC16 (rs7987202, rs9590408, and rs948182) and RCN3 (rs2946863, rs2878342, and rs3810194). Based on the network-based enrichment analysis, we found these 26 highlighted genes were jointly connected to exert effects on TB susceptibility. The co-expression patterns among these 26 genes were remarkably changed according to Mycobacterium tuberculosis (MTB) infection status. Based on 4 independent gene expression datasets, 21 of 26 genes (80.77%) showed significantly differential expressions between TB group and control group in mesenchymal stem cells, mice blood and lung tissues, as well as human alveolar macrophages. Together, we provide robust evidence to support 26 highlighted genes as important candidates for TB.