Identifying chronic obstructive pulmonary disease subtypes using multi-trait genetics
1
Issued Date
2025-03-01
Resource Type
eISSN
23523964
Scopus ID
2-s2.0-85218880862
Journal Title
eBioMedicine
Volume
113
Rights Holder(s)
SCOPUS
Bibliographic Citation
eBioMedicine Vol.113 (2025)
Suggested Citation
Ziyatdinov A., Hobbs B.D., Kanaan-Izquierdo S., Moll M., Sakornsakolpat P., Shrine N., Chen J., Song K., Bowler R.P., Castaldi P.J., Tobin M.D., Kraft P., Silverman E.K., Julienne H., Cho M.H., Aschard H. Identifying chronic obstructive pulmonary disease subtypes using multi-trait genetics. eBioMedicine Vol.113 (2025). doi:10.1016/j.ebiom.2025.105609 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/105556
Title
Identifying chronic obstructive pulmonary disease subtypes using multi-trait genetics
Author's Affiliation
Siriraj Hospital
Université Paris Cité
National Jewish Health
GlaxoSmithKline, USA
Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina
University of Leicester
Harvard T.H. Chan School of Public Health
Brigham and Women's Hospital
Glenfield Hospital
Universitat Politècnica de Catalunya
Institut de Recerca Sant Joan de Déu
Université Paris Cité
National Jewish Health
GlaxoSmithKline, USA
Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina
University of Leicester
Harvard T.H. Chan School of Public Health
Brigham and Women's Hospital
Glenfield Hospital
Universitat Politècnica de Catalunya
Institut de Recerca Sant Joan de Déu
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Chronic Obstructive Pulmonary Disease (COPD) has a broad spectrum of clinical characteristics. The aetiology of these differences is not well understood. The objective of this study is to assess whether respiratory genetic variants cluster by phenotype and associate with COPD heterogeneity. Methods: We clustered genome-wide association studies of COPD, lung function, and asthma and phenotypes from the UK Biobank using non-negative matrix factorization. We constructed cluster-specific genetic risk scores and tested these scores for association with phenotypes in non-Hispanic white subjects in the COPDGene study. Findings: We identified three clusters from 482 variants and 44 traits from genetic associations in 379,337 UK Biobank participants. Variants from asthma, COPD, and lung function were found in all three clusters. Clusters displayed varying effects on white blood cell counts, height, and body mass index (BMI)-related phenotypes in the UK Biobank. In the COPDGene cohort, cluster-specific genetic risk scores were associated with differences in steroid use, BMI, lymphocyte counts, and chronic bronchitis, as well as variations in gene and protein expression. Interpretation: Our results suggest that multi-phenotype analysis of obstructive lung disease-related risk variants may identify genetically driven phenotypic patterns in COPD. Funding: MHC was supported by R01HL149861, R01HL135142, R01HL137927, R01HL147148, and R01HL089856. HA and HJ were supported by ANR-20-CE36-0009-02 and ANR-16-CONV-0005. The COPDGene study (NCT00608764) is supported by grants from the NHLBI (U01HL089897 and U01HL089856), by NIH contract 75N92023D00011, and by the COPD Foundation through contributions made to an Industry Advisory Committee that has included AstraZeneca, Bayer Pharmaceuticals, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion.