Meta-Analysis

. 2022 May;161(5):1155-1166.

doi: 10.1016/j.chest.2021.12.674. Epub 2022 Jan 31.

Genetic Associations and Architecture of Asthma-COPD Overlap

Catherine John¹, Anna L Guyatt², Nick Shrine², Richard Packer², Thorunn A Olafsdottir³, Jiangyuan Liu⁴, Lystra P Hayden⁴, Su H Chu⁴, Jukka T Koskela⁵, Jian'an Luan⁶, Xingnan Li⁷, Natalie Terzikhan⁸, Hanfei Xu⁹, Traci M Bartz¹⁰, Hans Petersen¹¹, Shuguang Leng¹², Steven A Belinsky¹¹, Aivaras Cepelis¹³, Ana I Hernández Cordero¹⁴, Ma'en Obeidat¹⁴, Gudmar Thorleifsson¹⁵, Deborah A Meyers⁷, Eugene R Bleecker⁷, Lori C Sakoda¹⁶, Carlos Iribarren¹⁶, Yohannes Tesfaigzi¹⁷, Sina A Gharib¹⁸, Josée Dupuis¹⁰, Guy Brusselle¹⁹, Lies Lahousse²⁰, Victor E Ortega²¹, Ingileif Jonsdottir¹⁵, Don D Sin¹⁴, Yohan Bossé²², Maarten van den Berge²³, David Nickle²⁴, Jennifer K Quint²⁵, Ian Sayers²⁶, Ian P Hall²⁷, Claudia Langenberg⁶, Samuli Ripatti²⁸, Tarja Laitinen²⁹, Ann C Wu³⁰, Jessica Lasky-Su⁴, Per Bakke³¹, Amund Gulsvik³¹, Craig P Hersh⁴, Caroline Hayward³², Arnulf Langhammer¹³, Ben Brumpton³³, Kari Stefansson¹⁵, Michael H Cho⁴, Louise V Wain³⁴, Martin D Tobin³⁴

Affiliations

¹ Department of Health Sciences, University of Leicester, Leicester, England. Electronic address: cj153@leicester.ac.uk.
² Department of Health Sciences, University of Leicester, Leicester, England.
³ deCODE Genetics/Amgen, Reykjavik, Iceland.
⁴ Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
⁵ Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
⁶ MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, England.
⁷ Division of Genetics, Genomics and Precision Medicine, Department of Medicine, University of Arizona, Tucson, AZ.
⁸ Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.
⁹ Department of Biostatistics, Boston University School of Public Health, Boston, MA.
¹⁰ Cardiovascular Health Research Unit, Department of Medicine and Department of Biostatistics, University of Washington, Seattle, WA.
¹¹ Lovelace Respiratory Research Institute, Albuquerque, NM.
¹² Division of Epidemiology, Biostatistics, and Preventive Medicine, Department of Internal Medicine, University of New Mexico, Albuquerque, NM.
¹³ Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Levanger, Norway.
¹⁴ Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada.
¹⁵ deCODE Genetics/Amgen, Reykjavik, Iceland; Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
¹⁶ Division of Research, Kaiser Permanente of Northern California, Oakland, CA.
¹⁷ Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
¹⁸ Computational Medicine Core, Center for Lung Biology and UW Medicine Sleep Center, Medicine, University of Washington, Seattle, WA.
¹⁹ Department of Biostatistics, Boston University School of Public Health, Boston, MA; Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium.
²⁰ Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands; Department of Bioanalysis, Ghent University, Ghent, Belgium.
²¹ Department of Medicine, Wake Forest School of Medicine, Winston-Salem, NC.
²² Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec, QC, Canada.
²³ Department of Pulmonology, University Medical Center Groningen, University of Groningen, and GRIAC Research Institute, Groningen, the Netherlands.
²⁴ Global Health, University of Washington, Seattle, WA; Gossamer Bio, San Diego, CA.
²⁵ National Heart and Lung Institute, Imperial College London, London, UK.
²⁶ Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, England; Biodiscovery Institute, University of Nottingham, Nottingham, England.
²⁷ Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, England.
²⁸ Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA.
²⁹ Division of Medicine, Department of Pulmonary Diseases, Turku University Hospital, Turku, Finland; Department of Pulmonary Diseases and Clinical Allergology, University of Turku, Turku, Finland.
³⁰ Center for Healthcare Research in Pediatrics (CHeRP) and PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA.
³¹ Department of Clinical Science, University of Bergen, Bergen, Norway.
³² MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland.
³³ K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; Clinic of Thoracic and Occupational Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
³⁴ Department of Health Sciences, University of Leicester, Leicester, England; Leicester NIHR Biomedical Research Centre, Leicester, England.

PMID: 35104449
PMCID: PMC9131047
DOI: 10.1016/j.chest.2021.12.674

Meta-Analysis

Genetic Associations and Architecture of Asthma-COPD Overlap

Catherine John et al. Chest. 2022 May.

. 2022 May;161(5):1155-1166.

doi: 10.1016/j.chest.2021.12.674. Epub 2022 Jan 31.

Authors

Affiliations

¹ Department of Health Sciences, University of Leicester, Leicester, England. Electronic address: cj153@leicester.ac.uk.
² Department of Health Sciences, University of Leicester, Leicester, England.
³ deCODE Genetics/Amgen, Reykjavik, Iceland.
⁴ Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
⁵ Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
⁶ MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, England.
⁷ Division of Genetics, Genomics and Precision Medicine, Department of Medicine, University of Arizona, Tucson, AZ.
⁸ Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.
⁹ Department of Biostatistics, Boston University School of Public Health, Boston, MA.
¹⁰ Cardiovascular Health Research Unit, Department of Medicine and Department of Biostatistics, University of Washington, Seattle, WA.
¹¹ Lovelace Respiratory Research Institute, Albuquerque, NM.
¹² Division of Epidemiology, Biostatistics, and Preventive Medicine, Department of Internal Medicine, University of New Mexico, Albuquerque, NM.
¹³ Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Levanger, Norway.
¹⁴ Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada.
¹⁵ deCODE Genetics/Amgen, Reykjavik, Iceland; Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
¹⁶ Division of Research, Kaiser Permanente of Northern California, Oakland, CA.
¹⁷ Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
¹⁸ Computational Medicine Core, Center for Lung Biology and UW Medicine Sleep Center, Medicine, University of Washington, Seattle, WA.
¹⁹ Department of Biostatistics, Boston University School of Public Health, Boston, MA; Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium.
²⁰ Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands; Department of Bioanalysis, Ghent University, Ghent, Belgium.
²¹ Department of Medicine, Wake Forest School of Medicine, Winston-Salem, NC.
²² Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec, QC, Canada.
²³ Department of Pulmonology, University Medical Center Groningen, University of Groningen, and GRIAC Research Institute, Groningen, the Netherlands.
²⁴ Global Health, University of Washington, Seattle, WA; Gossamer Bio, San Diego, CA.
²⁵ National Heart and Lung Institute, Imperial College London, London, UK.
²⁶ Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, England; Biodiscovery Institute, University of Nottingham, Nottingham, England.
²⁷ Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, England.
²⁸ Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA.
²⁹ Division of Medicine, Department of Pulmonary Diseases, Turku University Hospital, Turku, Finland; Department of Pulmonary Diseases and Clinical Allergology, University of Turku, Turku, Finland.
³⁰ Center for Healthcare Research in Pediatrics (CHeRP) and PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA.
³¹ Department of Clinical Science, University of Bergen, Bergen, Norway.
³² MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland.
³³ K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; Clinic of Thoracic and Occupational Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
³⁴ Department of Health Sciences, University of Leicester, Leicester, England; Leicester NIHR Biomedical Research Centre, Leicester, England.

PMID: 35104449
PMCID: PMC9131047
DOI: 10.1016/j.chest.2021.12.674

Abstract

Background: Some people have characteristics of both asthma and COPD (asthma-COPD overlap), and evidence suggests they experience worse outcomes than those with either condition alone.

Research question: What is the genetic architecture of asthma-COPD overlap, and do the determinants of risk for asthma-COPD overlap differ from those for COPD or asthma?

Study design and methods: We conducted a genome-wide association study in 8,068 asthma-COPD overlap case subjects and 40,360 control subjects without asthma or COPD of European ancestry in UK Biobank (stage 1). We followed up promising signals (P < 5 × 10^-6) that remained associated in analyses comparing (1) asthma-COPD overlap vs asthma-only control subjects, and (2) asthma-COPD overlap vs COPD-only control subjects. These variants were analyzed in 12 independent cohorts (stage 2).

Results: We selected 31 independent variants for further investigation in stage 2, and discovered eight novel signals (P < 5 × 10^-8) for asthma-COPD overlap (meta-analysis of stage 1 and 2 studies). These signals suggest a spectrum of shared genetic influences, some predominantly influencing asthma (FAM105A, GLB1, PHB, TSLP), others predominantly influencing fixed airflow obstruction (IL17RD, C5orf56, HLA-DQB1). One intergenic signal on chromosome 5 had not been previously associated with asthma, COPD, or lung function. Subgroup analyses suggested that associations at these eight signals were not driven by smoking or age at asthma diagnosis, and in phenome-wide scans, eosinophil counts, atopy, and asthma traits were prominent.

Interpretation: We identified eight signals for asthma-COPD overlap, which may represent loci that predispose to type 2 inflammation, and serious long-term consequences of asthma.

Keywords: COPD; asthma; epidemiology; genome-wide association study; spirometry.

PubMed Disclaimer

Figures

**Figure 1**
Manhattan plot of association results for asthma-COPD overlap in stage 1 (UK Biobank). The x axis shows genomic location by chromosome, the y axis shows the –log₁₀P value, corrected for the intercept of linkage disequilibrium score regression (1.018). The eight top signals (from joint analysis) are highlighted in red, and labeled with rsIDs (reference SNP [single-nucleotide polymorphism] ID numbers). The black line indicates P = 5 × 10^–8 (commonly known as genome-wide significance), and the dotted line corresponds to P = 5 × 10^–6 (genome-wide suggestive threshold). A quantile-quantile plot is shown in e-Figure 1. For further details on the eight SNPs shown here, see also Table 2.

**Figure 2**
Genetic correlations between asthma-COPD overlap (ACO) and asthma, moderate-severe asthma, COPD, FEV₁/FVC, and blood eosinophil counts. Genetic correlations were computed by linkage disequilibrium score regression. The annotation in each tile represents the magnitude of the genetic correlation estimate (rG), and intensity is proportional to the magnitude of effect. Note that for FEV₁/FVC, a negative correlation shows that the other trait is associated with reduced FEV₁/FVC (reduced FEV₁/FVC implies worse lung function). Data sets used: ACO = current discovery results from UK Biobank; Asthma = GWAS results from Demenais et al; Asthma (moderate-severe) = genome-wide association study (GWAS) of asthma by Shrine et al; COPD = GWAS of COPD by Sakornsakolpat et al; FEV₁/FVC = GWAS of FEV₁/FVC (UK Biobank and SpiroMeta) by Shrine et al; Eosinophils = blood eosinophil counts published by Astle et al.

See this image and copyright information in PMC

Comment in

Understanding the Genetics of Asthma-COPD Overlap.
Hudler AC, Sharma S. Hudler AC, et al. Chest. 2022 May;161(5):1125-1126. doi: 10.1016/j.chest.2022.02.024. Chest. 2022. PMID: 35526879 No abstract available.

References

1. GBD Chronic Respiratory Disease Collaborators Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020;8(6):585–596. - PMC - PubMed
1. Bateman E.D., Reddel H.K., van Zyl-Smit R.N., Agusti A. The asthma-COPD overlap syndrome: towards a revised taxonomy of chronic airways diseases? Lancet Respir Med. 2015;3(9):719–728. - PubMed
1. Postma D.S., Rabe K.F. The asthma-COPD overlap syndrome. N Engl J Med. 2015;373(13):1241–1249. - PubMed
1. Global Initiative for Asthma; Global Initiative for Chronic Obstructive Lung Disease Diagnosis of Diseases of Chronic Airflow Limitation: Asthma, COPD, and Asthma-COPD Overlap Syndrome (ACOS). 2015. https://goldcopd.org/wp-content/uploads/2016/04/GOLD_ACOS_2015.pdf
1. Ghebre M.A., Bafadhel M., Desai D., et al. Biological clustering supports both “Dutch” and “British” hypotheses of asthma and chronic obstructive pulmonary disease. J Allergy Clin Immunol. 2015;135(1):63–72. - PMC - PubMed

Publication types

Actions
Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Grants and funding

LinkOut - more resources

Full Text Sources
Medical
- MedlinePlus Consumer Health Information
- MedlinePlus Health Information
Research Materials
- NCI CPTC Antibody Characterization Program

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Genetic Associations and Architecture of Asthma-COPD Overlap

Affiliations

Genetic Associations and Architecture of Asthma-COPD Overlap

Authors

Affiliations

Abstract

Figures

Comment in

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Research Materials