Background Immunoglobulin E (IgE) is highly related to a variety of atopic diseases, and several genome-wide correlation studies (GWASs) have demonstrated the association between genes and IgE. In this study, we conducted the largest genome-wide association study of IgE in a Taiwanese Han population and aimed to elucidate the genetic architecture of IgE. Methods Genome-wide association study was used to discover the association between variants and IgE. Through HLA imputation, we explored the association between HLA alleles and IgE. In order to explore the pleiotropy relationship between IgE and atopic diseases, we performed both global and local genetic correlation analysis. Moreover, we divided our cohort into a training group and a validation group to construct the polygenic risk score (PRS) of IgE and applied it to test the risk of asthma and atopic dermatitis. Results A total of 8 independent variants showed genome-wide significance, and rs147642819 at 6p21.32 was the most significant signal (p= 1.8×10 -19). Seven of the loci were replicated successfully after a meta-analysis of the Japanese population. Among all the HLA alleles, HLA-DQB1*03:03 is the most significant allele. The global genetic correlation showed significance between IgE and asthma. The IgE PRS significantly correlated with the total IgE level. Furthermore, the top 10 quantile IgE PRS group had a potentially higher risk for asthma, which was replicated in the Japanese population as well. Conclusions Our study provided a more comprehensive understanding of the impact of genomic variants including complex HLA alleles on serum IgE.

Background: Corrected QT (QTc) prolongation is a frequently observed ECG abnormality in patients with rheumatoid arthritis (RA). Objectives: We aimed to investigate the association between disease activity and QTc interval in patients with RA. Methods: Data were obtained from the Kyoto University Rheumatoid Arthritis Management Alliance population-based RA cohort. We used a linear model to compare the association between QTc interval and RA-related parameters, including patient characteristics and disease activity assessed using the visual analog scale, C-reactive protein level, erythrocyte sedimentation rate (ESR), disease activity score 28-joint count using erythrocyte sedimentation rate (DAS28-ESR), simplified disease activity index (SDAI), clinical disease activity index (CDAI), and health assessment questionnaire (HAQ) score. We also constructed multivariate linear regression models to adjust for confounding effects. Results: The mean QTc interval of 340 patients (mean age: 64.7 ± 12.3 years, female: 289 [85%]) with ECG data was 420.0 ± 18.4, and the mean disease activity indices were: DAS28-ESR, 2.7 ± 1.1 points; CDAI, 5.0 ± 5.2 points; SDAI, 5.4 ± 5.7 points; and HAQ, 0.61 ± 0.71 points. Linear correlations were observed between the QTc interval and all parameters for disease activity in the univariate analysis. The three multivariate linear regression models using age, sex, HAQ score, and disease activity indices (CDAI, SDAI, or DAS28-ESR) were significantly associated with the QTc interval (P = 0.0002, 0.0002, 0.0004, respectively). Conclusions: Disease activity is significantly associated with the QTc interval in patients with RA. Attention should be given to ECG abnormalities in patients with RA and progressive disease activity.