Genetic differences according to onset age and lung function in asthma: A cluster analysis
Abstract Background The 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 t...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-07-01
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| Series: | Clinical and Translational Allergy |
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| Online Access: | https://doi.org/10.1002/clt2.12282 |
| _version_ | 1797773205724725248 |
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| author | Han‐Kyul Kim Ji‐One Kang Ji Eun Lim Tae‐Woong Ha Hae Un Jung Won Jun Lee Dong Jun Kim Eun Ju Baek Ian M. Adcock Kian Fan Chung Tae‐Bum Kim Bermseok Oh |
| author_facet | Han‐Kyul Kim Ji‐One Kang Ji Eun Lim Tae‐Woong Ha Hae Un Jung Won Jun Lee Dong Jun Kim Eun Ju Baek Ian M. Adcock Kian Fan Chung Tae‐Bum Kim Bermseok Oh |
| author_sort | Han‐Kyul Kim |
| collection | DOAJ |
| description | Abstract Background The 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. Methods In 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. Results Clustering 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. Conclusions Our 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. |
| first_indexed | 2024-03-12T22:02:04Z |
| format | Article |
| id | doaj.art-678ea1947c254a438965a5bf833163f6 |
| institution | Directory Open Access Journal |
| issn | 2045-7022 |
| language | English |
| last_indexed | 2024-03-12T22:02:04Z |
| publishDate | 2023-07-01 |
| publisher | Wiley |
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| series | Clinical and Translational Allergy |
| spelling | doaj.art-678ea1947c254a438965a5bf833163f62023-07-25T04:32:34ZengWileyClinical and Translational Allergy2045-70222023-07-01137n/an/a10.1002/clt2.12282Genetic differences according to onset age and lung function in asthma: A cluster analysisHan‐Kyul Kim0Ji‐One Kang1Ji Eun Lim2Tae‐Woong Ha3Hae Un Jung4Won Jun Lee5Dong Jun Kim6Eun Ju Baek7Ian M. Adcock8Kian Fan Chung9Tae‐Bum Kim10Bermseok Oh11Department of Biochemistry and Molecular Biology School of Medicine Kyung Hee University Seoul KoreaDepartment of Biochemistry and Molecular Biology School of Medicine Kyung Hee University Seoul KoreaDepartment of Biochemistry and Molecular Biology School of Medicine Kyung Hee University Seoul KoreaDepartment of Biochemistry and Molecular Biology School of Medicine Kyung Hee University Seoul KoreaDepartment of Biomedical Science Graduate School Kyung Hee University Seoul KoreaDepartment of Biochemistry and Molecular Biology School of Medicine Kyung Hee University Seoul KoreaDepartment of Biomedical Science Graduate School Kyung Hee University Seoul KoreaDepartment of Biomedical Science Graduate School Kyung Hee University Seoul KoreaThe National Heart and Lung Institute Imperial College London UKThe National Heart and Lung Institute Imperial College London UKDepartment of Allergy and Clinical Immunology Asan Medical Center University of Ulsan College of Medicine Seoul KoreaDepartment of Biochemistry and Molecular Biology School of Medicine Kyung Hee University Seoul KoreaAbstract Background The 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. Methods In 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. Results Clustering 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. Conclusions Our 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.https://doi.org/10.1002/clt2.12282asthmacluster analysisgenome‐wide association study |
| spellingShingle | Han‐Kyul Kim Ji‐One Kang Ji Eun Lim Tae‐Woong Ha Hae Un Jung Won Jun Lee Dong Jun Kim Eun Ju Baek Ian M. Adcock Kian Fan Chung Tae‐Bum Kim Bermseok Oh Genetic differences according to onset age and lung function in asthma: A cluster analysis Clinical and Translational Allergy asthma cluster analysis genome‐wide association study |
| title | Genetic differences according to onset age and lung function in asthma: A cluster analysis |
| title_full | Genetic differences according to onset age and lung function in asthma: A cluster analysis |
| title_fullStr | Genetic differences according to onset age and lung function in asthma: A cluster analysis |
| title_full_unstemmed | Genetic differences according to onset age and lung function in asthma: A cluster analysis |
| title_short | Genetic differences according to onset age and lung function in asthma: A cluster analysis |
| title_sort | genetic differences according to onset age and lung function in asthma a cluster analysis |
| topic | asthma cluster analysis genome‐wide association study |
| url | https://doi.org/10.1002/clt2.12282 |
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