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Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma
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 reg...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457797/ https://www.ncbi.nlm.nih.gov/pubmed/32867763 http://dx.doi.org/10.1186/s12920-020-00768-z |
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author | Ma, Xiuqing Wang, Peilan Xu, Guobing Yu, Fang Ma, Yunlong |
author_facet | Ma, Xiuqing Wang, Peilan Xu, Guobing Yu, Fang Ma, Yunlong |
author_sort | Ma, Xiuqing |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7457797 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74577972020-09-02 Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma Ma, Xiuqing Wang, Peilan Xu, Guobing Yu, Fang Ma, Yunlong BMC Med Genomics Research Article 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. BioMed Central 2020-08-31 /pmc/articles/PMC7457797/ /pubmed/32867763 http://dx.doi.org/10.1186/s12920-020-00768-z Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Ma, Xiuqing Wang, Peilan Xu, Guobing Yu, Fang Ma, Yunlong Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma |
title | Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma |
title_full | Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma |
title_fullStr | Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma |
title_full_unstemmed | Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma |
title_short | Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma |
title_sort | integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457797/ https://www.ncbi.nlm.nih.gov/pubmed/32867763 http://dx.doi.org/10.1186/s12920-020-00768-z |
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