This study aims to develop an integrative predictive system that combines genomic variants, clinical data, environmental exposures, and lifestyle factors to improve risk prediction for common modern diseases including chronic diseases, cancers, geriatric syndromes, and psychiatric disorders. Utilizing the deeply phenotyped UK Biobank cohort, the research will focus on characterizing the complex interplay between genetics and external factors, identifying novel biomarkers and pathways, and constructing robust predictive models. These models will incorporate multi-dimensional data and advanced machine learning techniques to enhance early detection and personalized prevention strategies. The system will also be validated using independent datasets to ensure generalizability and clinical utility.
Research Questions:
1. What are the key genetic markers and external factors that most strongly influence disease risk in these conditions?
2. Can machine learning models leveraging multi-data identify novel predictors and biological pathways underlying these diseases?
3. To what extent does incorporating lifestyle and environmental variables improve predictive performance compared to genetic data alone?
Objectives:
1. To integrate comprehensive genomic, clinical, environmental exposure, and lifestyle data from the UK Biobank and other relevant datasets to establish a multi-dimensional platform for disease risk analysis.
2. To identify shared genetic and external factors that contribute to the onset and progression of these common modern diseases.
3. To provide actionable insights for early prevention and targeted interventions in high-risk populations based on integrated genetic susceptibility and modifiable environmental exposures.
Scientific Rationale:
Leveraging the extensive and deeply phenotyped UK Biobank cohort with comprehensive genomic data, this stud
