Principal Investigator: Professor Nilanjan Chatterjee
Department: Department of BiostatisticsTags: 17712, absolute risk, featured, gene-environment interactions, prevention
1a: The aim of the research is to use data from UK Biobank prospective cohort study to estimate joint risk of multiple common disease conditions, including cancer, type-2 diabetes, cardiovascular diseases, and overall mortality, associated with GWAS generated polygenic disease-risk scores and modifiable risk-factors such as smoking, BMI, alcohol and physical activity. The analysis will allow understanding of how the potential impact intervention on modifiable risk-factors, may or may not vary by individual’s genetic risk-profiles.
1b: The proposed research will generate valuable information regarding whether genetic information could be useful for targeting certain primary prevention efforts for risk-factor intervention that cannot be applied to the general population for cost and other burdens.
1c: For subjects in the UK Biobank cohort, polygenic risk-score (PRS) for major chronic diseases will be constructed based on published literature on susceptibility SNPs and their disease odds-ratios. Data from UK Biobank will be then used to estimate absolute risk of different disease endpoints and mortality in population strata defined by polygenic risk scores and modifiable risk-factors. For evaluating combined endpoint like overall mortality or overall cancer incidence, the disease-specific PRSs will be combined to form a composite PRS. Further, to understand the potential causal effect of intervention of modifiable risk-factors, like BMI, a Mendelian Randomization approach will be used to estimate absolute risk-reduction parameters associated with BMI for the different outcomes within strata defined by the disease associated PRS variables. These “instrumental” variable derived absolute-risk reduction parameters will then be then compared with more direct epidemiological estimate of the same parameters for the assessment of consistency of results across two types of analyses.
1d: All subjects in the full cohort with or without genotype data.