Complex diseases, e.g. cardiovascular, autoimmune disorders, account for the majority of global deaths and healthcare costs. These conditions are driven by a combination of genetic susceptibility and environmental exposures, including diet, physical activity, infections, and other modifiable lifestyle factors. Despite advances in genomics and prevention strategies, their global burden continues to rise, particularly as populations age and diversify.
We aim to uncover the genetic architecture and molecular mechanisms underlying complex diseases and improve risk prediction through the integrative analysis of multi-modal data including multi-omics, environmental, and clinical information.
Our research includes three main goals: 1) Identify genetic variants and biomolecular traits (e.g., proteins, metabolites) that influence disease susceptibility and progression, and explore their interrelationships using approaches such as multi-omic genetic prediction (e.g., through our established OmicsPred.org). 2) Investigate the interplay between genetic risk, molecular profiles, and environmental/lifestyle factors, to identify causal pathways and shared mechanisms underlying disease clusters and multimorbidity. 3) Develop and evaluate predictive models, including polygenic risk scores and multi-omic prediction models to improve risk prediction and stratification across populations and ancestries using machine learning.
We will leverage the scale and richness of UK Biobank, including genomic, biomarker, lifestyle, imaging, clinical, and environmental data, as well as newly available multi-omics (e.g. Olink proteomics) and longitudinal measurements. Ultimately, this research will (i) advance understanding of the molecular basis of common complex diseases and their complications; (ii) improve prediction tools that are generalizable across ancestries and populations; and (iii) support more effective and equitable strategies for disease prevention, early detection, and treatment.
