Rationale: Complex diseases arise from interplay of genetic predisposition & environmental factors. Polygenic Risk Scores (PRS) quantify genetic risk, while clinical data reflects environmental and phenotypic influences. Integrating these data sources in the UK Biobank offers unprecedented opportunities for improved disease prediction, mechanistic understanding, and therapeutic target identification.
Research Questions:
– How do PRS, combined with clinical data, improve prediction of common diseases compared to using either alone?
– Which clinical factors modify the impact of PRS on disease risk?
– Can integrating PRS and clinical data identify novel disease subtypes or endophenotypes?
– Do specific gene-environment interactions, revealed by PRS and clinical data, suggest potential therapeutic targets?
Objectives:
Develop predictive models for selected diseases using PRS and clinical data from UK Biobank.
Identify clinical factors that interact with PRS to influence disease risk.
Perform data-driven subtyping of diseases based on integrated genetic and clinical information.
Explore potential drug targets based on identified gene-environment interactions.
This outline focuses on feasibility within the character limit while covering core aspects.