Risk scores for diabetes risk prediction in the UK Biobank
Polygenic risk scores (PRSs) can potentially enhance risk stratification and prevention for chronic diseases, but the value of such scores for predicting diabetes is uncertain. Studies suggest the superiority of such genetic instruments for enhancing prediction of risk when added to conventional risk factors for other major chronic diseases. However, the value of such algorithms for predicting diabetes risk at a population level is unknown. Furthermore, the implications of using traditional versus genetic information to guide preventive and therapeutic interventions in diabetes, are unknown. The UK Biobank (UKB) offers an unique platform to comparatively asses the relevance of traditional and genetic risk scores to diabetes among 0.5 million adults with genotype information, lifestyle and biological measurements, including NMR metabolomics assays, biochemistry, proteomics, and a long follow-up for causes of hospitalisation and mortality.
The aims of the project include:
- To derive and validate PRSs for diabetes, using external genome-wide association studies and UKB-specific instruments
- To assess the additional value of the different tools for predicting diabetes incidence or mortality in the UKB population
- To assess the implications of the different predictive tools for prevention and treatment of diabetes at a population level.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The student will gain experience in genetic epidemiology and analysis of large-scale prospective data. They will develop skills in conducting systematic literature reviews, analytical techniques, research planning, statistical programming, data analysis, and presentation skills. The student will be supported to publish peer-reviewed papers during their DPhil.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
Training in advanced statistics, genetic epidemiology, statistical programming, and scientific writing will be provided. Attendance at seminars, workshops and courses provided by the Department and University will also be encouraged. The candidate will have the opportunity to present their research work at relevant international/national conferences.
Candidates should have a Masters degree in genetic epidemiology, biomedical sciences, or medical statistics. Proficeincy in conducting analyses with R, STATA, SAS or Python is essential.