Research Question:
How do multi-omic and environmental factors interact to influence the risk, progression, and mechanisms of complex diseases, and how can these insights enhance disease prediction and prevention?
Aims:
To uncover the biological and environmental drivers of complex diseases and integrate these findings into improved risk stratification and prevention strategies.
Objectives:
1. Integrate genomic, transcriptomic, epigenomic, and proteomic data to identify regulatory mechanisms underlying complex diseases and apply causal inference methods (e.g., Mendelian Randomization) to distinguish causal from confounded associations.
2. Model gene-environment interactions (e.g., genetics with lifestyle, diet, socioeconomic status) to determine their combined effects on disease risk and progression.
3. Apply machine learning (e.g., GLMNET, XGBoost) and traditional GWAS to detect novel genetic variants and gene-environment interactions influencing disease susceptibility.
4. Use neuroimaging data to estimate brain age acceleration and compare trajectories between neurological disease groups and healthy individuals.
5. Test candidate genetic variants for associations with complex diseases, providing functional insights into their roles.
6. Compare allele frequencies between disease-specific cohorts and controls to refine risk estimates and identify population-specific risk variants.
Validate significant GWAS findings reported in external studies across cardiovascular, metabolic, liver, cancer, and neurological diseases.
This project leverages the UK Biobank’s rich genomic, multi-omic, imaging, and environmental data to advance understanding of disease mechanisms and improve predictive and preventive approaches across multiple complex disease areas.