Dataset Information

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

ABSTRACT:

Background

Childhood-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.

Methods

In 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.

Results

Based 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.

Conclusions

We 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.

SUBMITTER: Ma X

PROVIDER: S-EPMC7457797 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

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

Ma Xiuqing X Wang Peilan P Xu Guobing G Yu Fang F Ma Yunlong Y

BMC medical genomics 20200831 1

<h4>Background</h4>Childhood-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.<h4>Methods</h4>In the current investigation, we conducted a two-stage designed She ...[more]

PMID: 32867763

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Project description:BackgroundGenome-wide association studies (GWASs) have identified thousands of variants associated with asthma and other complex diseases. However, the functional effects of most of these variants are unknown. Moreover, GWASs do not provide context-specific information on cell types or environmental factors that affect specific disease risks and outcomes. To address these limitations, we used an upper airway epithelial cell (AEC) culture model to assess transcriptional and epigenetic responses to rhinovirus (RV), an asthma-promoting pathogen, and provide context-specific functional annotations to variants discovered in GWASs of asthma.MethodsGenome-wide genetic, gene expression, and DNA methylation data in vehicle- and RV-treated upper AECs were collected from 104 individuals who had a diagnosis of airway disease (n=66) or were healthy participants (n=38). We mapped cis expression and methylation quantitative trait loci (cis-eQTLs and cis-meQTLs, respectively) in each treatment condition (RV and vehicle) in AECs from these individuals. A Bayesian test for colocalization between AEC molecular QTLs and adult onset asthma and childhood onset asthma GWAS SNPs, and a multi-ethnic GWAS of asthma, was used to assign the function to variants associated with asthma. We used Mendelian randomization to demonstrate DNA methylation effects on gene expression at asthma colocalized loci.ResultsAsthma and allergic disease-associated GWAS SNPs were specifically enriched among molecular QTLs in AECs, but not in GWASs from non-immune diseases, and in AEC eQTLs, but not among eQTLs from other tissues. Colocalization analyses of AEC QTLs with asthma GWAS variants revealed potential molecular mechanisms of asthma, including QTLs at the TSLP locus that were common to both the RV and vehicle treatments and to both childhood onset and adult onset asthma, as well as QTLs at the 17q12-21 asthma locus that were specific to RV exposure and childhood onset asthma, consistent with clinical and epidemiological studies of these loci.ConclusionsThis study provides evidence of functional effects for asthma risk variants in AECs and insight into RV-mediated transcriptional and epigenetic response mechanisms that modulate genetic effects in the airway and risk for asthma.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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