This project aims to leverage the extensive genomic and clinical resources of the UK Biobank cohort to systematically explore the relationships between genetic variations and diverse clinical phenotypes. The central research questions are:
(1) Which genetic variants are significantly associated with disease onset, progression, and prognosis? (2) How do pleiotropic variants influence multiple phenotypic traits across different disease domains? (3) Can integrated genomic and clinical data improve the accuracy of risk prediction models beyond conventional approaches?
The objectives of this study are threefold. First, to conduct comprehensive genome-wide and phenome-wide association studies (GWAS and PheWAS) to identify genetic determinants of complex traits and diseases. Second, to integrate genomic data with clinical, lifestyle, and environmental information, thereby uncovering gene-environment interactions and pleiotropic effects. Third, to employ advanced machine learning and artificial intelligence methods to develop predictive models for disease risk, stratification, and prognosis, facilitating translation into precision medicine applications.
The scientific rationale is grounded in the unprecedented scale and depth of the UK Biobank dataset, which offers both statistical power and diversity to detect novel associations. Traditional single-omics or genotype-only studies are limited in their ability to capture the complexity of human health and disease. By unifying genomic, clinical, and environmental data, this project will provide deeper biological insights into disease mechanisms, reveal novel therapeutic targets, and establish robust predictive frameworks.
Ultimately, the findings are expected to contribute to early disease detection, risk stratification, and personalized interventions, thereby advancing precision medicine and improving healthcare outcomes on a population-wide scale.
