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Integrative genomics analysis identifies promising SNPs and genes implicated in tuberculosis risk based on multiple omics datasets.

ABSTRACT: 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.

SUBMITTER: Xu M

PROVIDER: S-EPMC7732298 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Integrative genomics analysis identifies promising SNPs and genes implicated in tuberculosis risk based on multiple omics datasets.

Xu Mengqiu M Li Jingjing J Xiao Zhaoying Z Lou Jiongpo J Pan Xinrong X Ma Yunlong Y

Aging 20201013 19

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 ...[more]

PMID: 33051402

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Project description:A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms, and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: (1) treat the biomarker identification in each single study as a task and then combine them by multitask learning; (2) group variables from all studies for identifying significant genes; (3) enforce sparse constraint on groups of variables to overcome the "small sample, but large variables" problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E, and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed from other studies.

Project description: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). With the use of a series of bioinformatics analyses, we highlighted 11 important genes such as GNGT2, TLR6, and TTC19 as authentic risk genes associated with moderate-to-severe/severe asthma. With respect to GNGT2, there were 3 eSNPs of rs17637472 (PeQTL?=?2.98?×?10-?8 and PGWAS?=?3.40?×?10-?8), rs11265180 (PeQTL?=?6.0?×?10-?6 and PGWAS?=?1.99?×?10-?3), and rs1867087 (PeQTL?=?1.0?×?10-?4 and PGWAS?=?1.84?×?10-?5) identified. In addition, GNGT2 is 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 (Anova P?=?1.55?×?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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Integrative genomics analysis identifies promising SNPs and genes implicated in tuberculosis risk based on multiple omics datasets.

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Integrative genomics analysis identifies promising SNPs and genes implicated in tuberculosis risk based on multiple omics datasets.

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