Translational Analysis of Moderate to Severe Asthma GWAS Signals Into Candidate Causal Genes and Their Functional, Tissue-Dependent and Disease-Related Associations.

GWAS SNP causal genes eQTL moderate to severe asthma

Journal

Frontiers in allergy
ISSN: 2673-6101
Titre abrégé: Front Allergy
Pays: Switzerland
ID NLM: 9918227355906676

Informations de publication

Date de publication:
2021
Historique:
received: 09 07 2021
accepted: 06 09 2021
entrez: 7 4 2022
pubmed: 8 4 2022
medline: 8 4 2022
Statut: epublish

Résumé

Asthma affects more than 300 million people globally and is both under diagnosed and under treated. The most recent and largest genome-wide association study investigating moderate to severe asthma to date was carried out in 2019 and identified 25 independent signals. However, as new and in-depth downstream databases become available, the translational analysis of these signals into target genes and pathways is timely. In this study, unique (U-BIOPRED) and publicly available datasets (HaploReg, Open Target Genetics and GTEx) were investigated for the 25 GWAS signals to identify 37 candidate causal genes. Additional traits associated with these signals were identified through PheWAS using the UK Biobank resource, with asthma and eosinophilic traits amongst the strongest associated. Gene expression omnibus dataset examination identified 13 candidate genes with altered expression profiles in the airways and blood of asthmatic subjects, including

Identifiants

pubmed: 35386986
doi: 10.3389/falgy.2021.738741
pmc: PMC8974692
doi:

Types de publication

Journal Article

Langues

eng

Pagination

738741

Informations de copyright

Copyright © 2021 Portelli, Rakkar, Hu, Guo, Adcock and Sayers.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Michael A Portelli (MA)

Centre for Respiratory Research, Translational Medical Sciences, School of Medicine, National Institute for Health Research Nottingham Biomedical Research Centre, Nottingham University Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom.

Kamini Rakkar (K)

Centre for Respiratory Research, Translational Medical Sciences, School of Medicine, National Institute for Health Research Nottingham Biomedical Research Centre, Nottingham University Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom.

Sile Hu (S)

Data Science Institute, Imperial College London, London, United Kingdom.

Yike Guo (Y)

Data Science Institute, Imperial College London, London, United Kingdom.

Ian M Adcock (IM)

The National Heart and Lung Institute, Imperial College London, London, United Kingdom.

Ian Sayers (I)

Centre for Respiratory Research, Translational Medical Sciences, School of Medicine, National Institute for Health Research Nottingham Biomedical Research Centre, Nottingham University Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom.

Classifications MeSH