<b>Exposome-Wide Gene-Environment Interaction </b><b>Screening for Blindness</b><b>-causing Diseases</b>
Creators: name: Guo, Ju
Publisher: name: figshare
Year: 2025
Subjects: classificationCode: 321201
subject: Ophthalmology

description: Objective With the rapid aging of the global population and continuous changes in lifestyle, cataract, glaucoma, age-related macular degeneration (AMD), myopia, and diabetic retinopathy (DR) have emerged as major blinding eye diseases, posing a severe threat to public health and socio-economic development. This study aimed to systematically elucidate the genetic susceptibility of these five major blinding eye diseases and to construct a comprehensive exposome dataset. Through Exposome-Wide Association Studies (ExWAS) and Gene-Environment Interaction (G×E) analyses, the study sought to comprehensively reveal how genetic factors and environmental exposures synergistically regulate the occurrence and progression of these diseases, thereby providing robust theoretical evidence and data support for early screening, risk stratification, and personalized intervention strategies.Methods Based on publicly available genome-wide association studies (GWAS) data of five major blinding eye diseases in 965,000 European individuals, this study conducted a meta-analysis to identify significant variants associated with these diseases. Leveraging the meta-GWAS results, polygenic risk scores (PRS) were constructed for each disease using genotype data from 335,824 unrelated White British participants in the UK Biobank (UKBB) target cohort. Integrating phenotypic information from the target cohort, we established a comprehensive exposome dataset encompassing multidimensional external exposures (e.g., lifestyle, dietary habits, environmental pollution) and internal exposures (including blood omics, metabolomics, proteomics, and imaging features).For external exposome-wide association studies (ExWAS), Cox proportional hazards regression models and logistic regression models were employed. Internal ExWAS analyses utilized linear regression and Bayesian hierarchical models. Additionally, the Light Gradient Boosting Machine (LightGBM) framework was applied to develop an integrated diagnostic model for internal exposure biomarkers in blinding eye diseases, significantly enhancing predictive accuracy.In gene-environment interaction analyses, both PRS-level (PRS×E) and individual variant-level (Variant×E) interactions were evaluated using Cox proportional hazards and logistic regression models. We further investigated the regulatory effects of PRS×E interactions on internal exposure biomarkers. Finally, based on these findings, the BlindEye-G×E interactive analysis platform was developed to enable online querying and visualization of genetic and environmental risk signals.ResultsMeta-GWAS of AMD and glaucoma identified three novel genetic loci, including NOA1, OCA2, and MTUS1. Genetic markers of these five major blinding eye diseases revealed a high level of consistency between discovery cohort and target cohort (Spearman correlation P &lt; 2.2E-16). The constructed PRS demonstrated strong risk stratification for AMD, DR, and glaucoma, with AUCs exceeding 0.75.The comprehensive exposome dataset covers 2,221 external exposure factors and 5,959 internal exposure indicators. External ExWAS identified 1,669 environmental or behavioral factors significantly linked to blinding eye diseases. This analysis not only revalidated the impact of traditional risk factors like smoking and alcohol consumption but also revealed that higher greenspace percentage in residential areas has a notable protective effect against cataracts. In contrast, internal ExWAS uncovered 2,999 significant associations, highlighting the potential of blood biomarkers, metabolic health status, ocular imaging parameters, and brain MRI for early disease diagnosis,especially in the pathological evaluation of DR, glaucoma, and myopia. G×E interaction analysis detected 954 significant PRS×E interaction signals (including 775 related to incident cases and 179 linked to prevalent cases) as well as 1,795 Variant×E interaction signals. These findings further clarify how external factors such as smoking, vitamin intake, and education level modulate key gene expression to jointly influence the onset and progression of eye diseases. Based on these integrated data, this study successfully constructed the first three-dimensional “gene-environment-phenotype” risk map for blinding eye diseases and developed the BlindEye-G×E interaction analysis platform, which facilitates the visual querying of risk signals and supports subsequent targeted screening and precision interventions.Conclusion This study integrates multi-omics data with exposomics analyses to systematically elucidate the genetic and environmental mechanisms underlying five major blinding eye diseases and successfully constructs a gene-environment interaction network. The findings not only reveal the pivotal role of genetic susceptibility in these diseases but also confirm that environmental exposure factors independently and synergistically regulate disease risk. In particular, through ExWAS and G×E analyses, a series of novel environmental protective factors and risk factors with potential intervention significance were identified, providing new theoretical and practical directions for early diagnosis, risk stratification, and personalized treatment. Moreover, the development of the BlindEye-G×E online platform offers a convenient tool for presenting research findings and sharing data, further advancing precision medicine for blinding eye diseases.