Investigate the importance of genetic data, over and above detailed epidemiological data, in multivariable prognostic models that predict risk of: (i) major morbidity, (ii) all-cause mortality
Approved Research ID: 23203
Approval date: June 17th 2020
The main aim is to investigate the importance of genetic data, over and above detailed epidemiological data, in multivariable prognostic models that predict risk of: (i) major morbidity (defined as: cardiovascular disease, stroke, cancer, or diabetes), (ii) all-cause mortality. Our primary questions are:
(1) How accurately can the risk of major morbidity and mortality be estimated using multivariable prognostic models based on detailed epidemiological risk factor data?
(2) Can such prognostic models be significantly improved by including genotype data, in the form of polygenic risk scores?
(3) What is the genetic predictive ability in individuals with otherwise healthy profiles?
We aim to assess the predictive ability of clinical data (biometrics and biomarkers), lifestyle data (smoking, diet and exercise) and genetic predisposition for common complex disorders such as coronary artery disease, breast cancer, stroke and type 2 diabetes.
Models will be created between genetic and other predictors, using classical statistical, machine learning and deep learning methods to research mortality and major morbidity outcomes in the UK population.
This risk modelling is health related and of public interest, since it increases our understanding of the factors related to development of disease, with the future potential of managing these risk factors to reduce incidence of disease. This project is part of a collaboration with RGA Reinsurance Company; employees at RGA have no access to UK Biobank data.