Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.


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 and biochemistry, 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.


Training opportunities will be available as required. Attendance at seminars, workshops and courses provided by the Department and the University of Oxford will be encouraged. By the end of the DPhil, the student will be competent to plan, undertake and interpret different types of statistical analyses, including methodology for building, validating and interpreting risk prediction models for clinical use. The student will learn to manage and analyse large epidemiological data, publish their results, and enhance presentation skills by reporting findings at conferences. The student will work within a multi-disciplinary team, and will gain experience in conducting systematic literature reviews, as well as statistical and epidemiological analyses.


Candidates should have postgraduate training in biomedical sciences, medical statistics, and genetic epidemiology. Proficiency with SAS, Stata and R are essential.