Research questions: The overall goal of this project is to assess the causal and interaction relationship between genetic, biomarker and environmental risk factors with chronic diseases and multimorbidity by incorporating the core data, measures and multi-omics data in UK Biobank cohort.
Objectives:
1) Examine causal relationships between known and novel risk factors and chronic disease or multimorbidity outcomes using Mendelian randomization.
2) Estimate the associations of trajectory changes, interactions, mediation effect, and moderation of modifiable environmental, metabolic factors and imaging with the risk and mortality of chronic diseases and multimorbidity.
3) Establish multigene risk scores and risk prediction models incorporating genetic, metabolic, biomarker, and environmental factors for chronic and multimorbid conditions through an interpretable machine learning approach.
4) Investigate a phenome-wide association study for chronic diseases and multimorbidity using MR-PheWAS and machine learning.
Scientific rationale for the research: Although large scaled cohort studies have found many associations between environmental factors or biomarkers and chronic diseases, little is known about the causal combinations underlying these associations. Our previous work has identified numerous major predictors that contribute to the development of multimorbidity, but the interaction between genetic, biomarkers, metabolic, and environmental factors for the development of multimorbidity and mortality needs to be examined in future research. This unique cohort will allow us to examine whether some combinations of biomarkers, environment, genetic, multi-omics and systemic diseases are associated with a higher risks of premature multimorbidity compared with other combinations.
