LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information.

Gene-environment (GE) interactions are essential in understanding human complex traits. Identifying these interactions is necessary for deciphering the biological basis of such traits. In this study, we review state-of-art methods for estimating the proportion of phenotypic variance explained by genome-wide GE interactions and introduce a novel statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (LDER-GE). LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics. LDER-GE leverages the complete Linkage Disequilibrium (LD) matrix, as opposed to only the diagonal squared LD matrix utilized by LDSC (Linkage Disequilibrium Score)-based methods. Our extensive simulation studies demonstrate that LDER-GE performs better than LDSC-based approaches by enhancing statistical efficiency by ~23%. This improvement is equivalent to a sample size increase of around 51%. Additionally, LDER-GE effectively controls type-I error rate and produces unbiased results. We conducted an analysis using UK Biobank data, comprising 307 259 unrelated European-Ancestry subjects and 966 766 variants, across 217 environmental covariate-phenotype (E-Y) pairs. LDER-GE identified 34 significant E-Y pairs while LDSC-based method only identified 23 significant E-Y pairs with 22 overlapped with LDER-GE. Furthermore, we employed LDER-GE to estimate the aggregated variance component attributed to multiple GE interactions, leading to an increase in the explained phenotypic variance with GE interactions compared to considering main genetic effects only. Our results suggest the importance of impacts of GE interactions on human complex traits.

A mediation analysis framework based on variance component to remove genetic confounding effect.

Identification of pleiotropy at the single nucleotide polymorphism (SNP) level provides valuable insights into shared genetic signals among phenotypes. One approach to study these signals is through mediation analysis, which dissects the total effect of a SNP on the outcome into a direct effect and an indirect effect through a mediator. However, estimated effects from mediation analysis can be confounded by the genetic correlation between phenotypes, leading to inaccurate results. To address this confounding effect in the context of genetic mediation analysis, we propose a restricted-maximum-likelihood (REML)-based mediation analysis framework called REML-mediation, which can be applied to either individual-level or summary statistics data. Simulations demonstrated that REML-mediation provides unbiased estimates of the true cross-trait causal effect, assuming certain assumptions, albeit with a slightly inflated standard error compared to traditional linear regression. To validate the effectiveness of REML-mediation, we applied it to UK Biobank data and analyzed several mediator-outcome trait pairs along with their corresponding sets of pleiotropic SNPs. REML-mediation successfully identified and corrected for genetic confounding effects in these trait pairs, with correction magnitudes ranging from 7% to 39%. These findings highlight the presence of genetic confounding effects in cross-trait epidemiological studies and underscore the importance of accounting for them in data analysis.

Supernatural Beliefs-Based Intervention to Improve Type-2 Diabetes Self-Management: A Pilot Randomized Controlled Trial from China.

China has over 100 million people living with type 2 diabetes mellitus (T2DM). Interventions framed around pre-existing personal beliefs in the supernatural may improve T2DM self-management, but such interventions are lacking in China. This pilot randomized controlled trial (RCT) assessed the feasibility of a full-scale RCT to evaluate the efficacy of a supernatural beliefs-based intervention on T2DM management self-efficacy in China. In 2019, 62 T2DM patients were enrolled at two hospitals in Suzhou, China. Participants were randomly assigned to view a 30-s control or intervention video at baseline. The control video showed general diabetes self-management information. The intervention video showed identical information, but also indicated that some diabetics with supernatural beliefs (chao ziran xinnian) have lower glycemic levels, because their beliefs enhance their confidence in diabetes self-management. Development of the intervention was guided by the theory of planned behavior and literature on spiritual framing health interventions. Baseline and follow-up measures after two weeks were assessed by interviewer administered surveys in-person and by telephone, respectively. Diabetes management self-efficacy was assessed with the diabetes management self-efficacy scale. Randomization of intervention allocation appeared to be successful. However, follow-up retention was low, especially for the intervention group (3% vs. 31%). A full-size efficacy RCT using the current study design is unlikely to succeed. T2DM patients shown the supernatural beliefs-based intervention had significantly higher loss to follow-up that was insurmountable. T2DM patients in Suzhou, China may not be receptive to brief, non-tailored supernatural beliefs-based interventions delivered to a general population in clinical settings.

Arrhythmia in patients with systemic sclerosis: incidence, risk factors and impact on mortality in a Swedish register-based study.

OBJECTIVES: The objectives of this study are to study the risk of developing cardiac arrhythmia and its subtypes over time in patients with systemic sclerosis (SSc), to assess potential risk factors for arrhythmia in SSc and to explore whether arrhythmia is associated with mortality. METHODS: We used nationwide Swedish registers to identify patients with incident SSc 2004-2019 and matched general population comparators (1:10). The primary outcome was incident arrhythmia. Follow-up started at the date of SSc diagnosis and ended at the primary outcome, death, emigration or 31 December 2019. We estimated the incidence of arrhythmia overall and stratified by subtype and explored the relative risk in relation to time since diagnosis using flexible parametric models. We used Cox regression to study risk factors for arrhythmia and the association of arrhythmia with mortality. RESULTS: We identified 1565 patients and 16 009 comparators. The overall incidence of arrhythmia was 255 (95% CI 221 to 295) and 119 (95% CI 112 to 127) per 10 000 person years in patients with SSc and comparators, respectively, corresponding to an IRR of 2.1 (95% CI 1.8 to 2.5). The greatest hazard difference between patients with SSc compared with the comparators was seen in the first year of follow-up (HR for arrhythmia 3.0; 95% CI 2.3 to 3.8). Atrial fibrillation and flutter were the most common arrhythmia subtypes. Male sex, index age and pulmonary arterial hypertension were significant risk factors for arrhythmia in SSc. Incident arrhythmia was significantly associated with mortality (HR 2.2; 95% CI 1.6 to 3.0). CONCLUSION: SSc is associated with higher incidence of cardiac arrhythmia compared with general population. Arrhythmia seems to be an early manifestation of SSc and is associated with higher mortality.