Genetic differences according to onset age and lung function in asthma: A cluster analysis

Author:

Kim Han‐Kyul1ORCID,Kang Ji‐One1,Lim Ji Eun1,Ha Tae‐Woong1,Jung Hae Un2,Lee Won Jun1,Kim Dong Jun2,Baek Eun Ju23,Adcock Ian M.4,Chung Kian Fan4ORCID,Kim Tae‐Bum5ORCID,Oh Bermseok123ORCID

Affiliation:

1. Department of Biochemistry and Molecular Biology School of Medicine Kyung Hee University Seoul Korea

2. Department of Biomedical Science Graduate School Kyung Hee University Seoul Korea

3. Mendel Seoul Korea

4. The National Heart and Lung Institute Imperial College London UK

5. Department of Allergy and Clinical Immunology Asan Medical Center University of Ulsan College of Medicine Seoul Korea

Abstract

AbstractBackgroundThe extent of differences between genetic risks associated with various asthma subtypes is still unknown. To better understand the heterogeneity of asthma, we employed an unsupervised method to identify genetic variants specifically associated with asthma subtypes. Our goal was to gain insight into the genetic basis of asthma.MethodsIn this study, we utilized the UK Biobank dataset to select asthma patients (All asthma, n = 50,517) and controls (n = 283,410). We excluded 14,431 individuals who had no information on predicted values of forced expiratory volume in one second percent (FEV1%) and onset age, resulting in a final total of 36,086 asthma cases. We conducted k‐means clustering based on asthma onset age and predicted FEV1% using these samples (n = 36,086). Cluster‐specific genome‐wide association studies were then performed, and heritability was estimated via linkage disequilibrium score regression. To further investigate the pathophysiology, we conducted eQTL analysis with GTEx and gene‐set enrichment analysis with FUMA.ResultsClustering resulted in four distinct clusters: early onset asthmanormalLF (early onset with normal lung function, n = 8172), early onset asthmareducedLF (early onset with reduced lung function, n = 8925), late‐onset asthmanormalLF (late‐onset with normal lung function, n = 12,481), and late‐onset asthmareducedLF (late‐onset with reduced lung function, n = 6508). Our GWASs in four clusters and in All asthma sample identified 5 novel loci, 14 novel signals, and 51 cluster‐specific signals. Among clusters, early onset asthmanormalLF and late‐onset asthmareducedLF were the least correlated (rg = 0.37). Early onset asthmareducedLF showed the highest heritability explained by common variants (h2 = 0.212) and was associated with the largest number of variants (71 single nucleotide polymorphisms). Further, the pathway analysis conducted through eQTL and gene‐set enrichment analysis showed that the worsening of symptoms in early onset asthma correlated with lymphocyte activation, pathogen recognition, cytokine receptor activation, and lymphocyte differentiation.ConclusionsOur findings suggest that early onset asthmareducedLF was the most genetically predisposed cluster, and that asthma clusters with reduced lung function were genetically distinct from clusters with normal lung function. Our study revealed the genetic variation between clusters that were segmented based on onset age and lung function, providing an important clue for the genetic mechanism of asthma heterogeneity.

Funder

National Research Foundation of Korea

Publisher

Wiley

Subject

Immunology and Allergy,Immunology,Pulmonary and Respiratory Medicine

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