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

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