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Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets

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 mech...

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Autores principales: Dong, Zhouzhou, Ma, Yunlong, Zhou, Hua, Shi, Linhui, Ye, Gongjie, Yang, Lei, Liu, Panpan, Zhou, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568423/
https://www.ncbi.nlm.nih.gov/pubmed/33066754
http://dx.doi.org/10.1186/s12890-020-01303-7
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author Dong, Zhouzhou
Ma, Yunlong
Zhou, Hua
Shi, Linhui
Ye, Gongjie
Yang, Lei
Liu, Panpan
Zhou, Li
author_facet Dong, Zhouzhou
Ma, Yunlong
Zhou, Hua
Shi, Linhui
Ye, Gongjie
Yang, Lei
Liu, Panpan
Zhou, Li
author_sort Dong, Zhouzhou
collection PubMed
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 (P(eQTL) = 2.98 × 10(− 8) and P(GWAS) = 3.40 × 10(− 8)), rs11265180 (P(eQTL) = 6.0 × 10(− 6) and P(GWAS) = 1.99 × 10(− 3)), and rs1867087 (P(eQTL) = 1.0 × 10(− 4) and P(GWAS) = 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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spelling pubmed-75684232020-10-20 Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets Dong, Zhouzhou Ma, Yunlong Zhou, Hua Shi, Linhui Ye, Gongjie Yang, Lei Liu, Panpan Zhou, Li BMC Pulm Med Research Article 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 (P(eQTL) = 2.98 × 10(− 8) and P(GWAS) = 3.40 × 10(− 8)), rs11265180 (P(eQTL) = 6.0 × 10(− 6) and P(GWAS) = 1.99 × 10(− 3)), and rs1867087 (P(eQTL) = 1.0 × 10(− 4) and P(GWAS) = 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. BioMed Central 2020-10-16 /pmc/articles/PMC7568423/ /pubmed/33066754 http://dx.doi.org/10.1186/s12890-020-01303-7 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
Dong, Zhouzhou
Ma, Yunlong
Zhou, Hua
Shi, Linhui
Ye, Gongjie
Yang, Lei
Liu, Panpan
Zhou, Li
Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets
title Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets
title_full Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets
title_fullStr Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets
title_full_unstemmed Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets
title_short Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets
title_sort integrated genomics analysis highlights important snps and genes implicated in moderate-to-severe asthma based on gwas and eqtl datasets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568423/
https://www.ncbi.nlm.nih.gov/pubmed/33066754
http://dx.doi.org/10.1186/s12890-020-01303-7
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