Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma

Abstract The number of genetic factors associated with asthma remains limited. To identify new genes with an undetected individual effect but collectively influencing asthma risk, we conducted a network-assisted analysis that integrates outcomes of genome-wide association studies (GWAS) and protein-...

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Autores principales:	Y. Liu, M. Brossard, C. Sarnowski, A. Vaysse, M. Moffatt, P. Margaritte-Jeannin, F. Llinares-López, M. H. Dizier, M. Lathrop, W. Cookson, E. Bouzigon, F. Demenais
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3f51af61fea74110990d66ccbe5319cf
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spelling	oai:doaj.org-article:3f51af61fea74110990d66ccbe5319cf2021-12-02T16:06:43ZNetwork-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma10.1038/s41598-017-01058-y2045-2322https://doaj.org/article/3f51af61fea74110990d66ccbe5319cf2017-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01058-yhttps://doaj.org/toc/2045-2322Abstract The number of genetic factors associated with asthma remains limited. To identify new genes with an undetected individual effect but collectively influencing asthma risk, we conducted a network-assisted analysis that integrates outcomes of genome-wide association studies (GWAS) and protein-protein interaction networks. We used two GWAS datasets, each consisting of the results of a meta-analysis of nine childhood-onset asthma GWASs (5,924 and 6,043 subjects, respectively). We developed a novel method to compute gene-level P-values (fastCGP), and proposed a parallel dense-module search and cross-selection strategy to identify an asthma-associated gene module. We identified a module of 91 genes with a significant joint effect on childhood-onset asthma (P < 10−5). This module contained a core subnetwork including genes at known asthma loci and five peripheral subnetworks including relevant candidates. Notably, the core genes were connected to APP (encoding amyloid beta precursor protein), a major player in Alzheimer’s disease that is known to have immune and inflammatory components. Functional analysis of the module genes revealed four gene clusters involved in innate and adaptive immunity, chemotaxis, cell-adhesion and transcription regulation, which are biologically meaningful processes that may underlie asthma risk. Our findings provide important clues for future research into asthma aetiology.Y. LiuM. BrossardC. SarnowskiA. VaysseM. MoffattP. Margaritte-JeanninF. Llinares-LópezM. H. DizierM. LathropW. CooksonE. BouzigonF. DemenaisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Y. Liu M. Brossard C. Sarnowski A. Vaysse M. Moffatt P. Margaritte-Jeannin F. Llinares-López M. H. Dizier M. Lathrop W. Cookson E. Bouzigon F. Demenais Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma
description	Abstract The number of genetic factors associated with asthma remains limited. To identify new genes with an undetected individual effect but collectively influencing asthma risk, we conducted a network-assisted analysis that integrates outcomes of genome-wide association studies (GWAS) and protein-protein interaction networks. We used two GWAS datasets, each consisting of the results of a meta-analysis of nine childhood-onset asthma GWASs (5,924 and 6,043 subjects, respectively). We developed a novel method to compute gene-level P-values (fastCGP), and proposed a parallel dense-module search and cross-selection strategy to identify an asthma-associated gene module. We identified a module of 91 genes with a significant joint effect on childhood-onset asthma (P < 10−5). This module contained a core subnetwork including genes at known asthma loci and five peripheral subnetworks including relevant candidates. Notably, the core genes were connected to APP (encoding amyloid beta precursor protein), a major player in Alzheimer’s disease that is known to have immune and inflammatory components. Functional analysis of the module genes revealed four gene clusters involved in innate and adaptive immunity, chemotaxis, cell-adhesion and transcription regulation, which are biologically meaningful processes that may underlie asthma risk. Our findings provide important clues for future research into asthma aetiology.
format	article
author	Y. Liu M. Brossard C. Sarnowski A. Vaysse M. Moffatt P. Margaritte-Jeannin F. Llinares-López M. H. Dizier M. Lathrop W. Cookson E. Bouzigon F. Demenais
author_facet	Y. Liu M. Brossard C. Sarnowski A. Vaysse M. Moffatt P. Margaritte-Jeannin F. Llinares-López M. H. Dizier M. Lathrop W. Cookson E. Bouzigon F. Demenais
author_sort	Y. Liu
title	Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma
title_short	Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma
title_full	Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma
title_fullStr	Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma
title_full_unstemmed	Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma
title_sort	network-assisted analysis of gwas data identifies a functionally-relevant gene module for childhood-onset asthma
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/3f51af61fea74110990d66ccbe5319cf
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Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma

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