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.


Type 2 diabetes (T2D) is a global health challenge with a rapidly increasing prevalence, causing significant morbidity and mortality. T2D is a complex disease, resulting from defects in several physiological pathways, including β-cell insufficiency, fat accumulation, and dysfunctional insulin signalling. Proteins play a pivotal role in human health and are targets for most drug treatment. The integration of proteomics and genomics has emerged as a transformative approach in the quest to identify novel drug targets for T2D and related metabolic disorders.

This proposal will use available and emerging proteomic data in the China Kadoorie Biobank (CKB: ~10,000 proteins in 4000 participants) and in UK Biobank (UKB: ~3000 proteins in 50,000 participants), with genomic data and other conventional T2D risk factors and T2D related traits including glucose and haemoglobin A1c.


The specific DPhil project will be developed according to the candidate’s interests and aptitude, and may cover some of the following objectives:

  • to discover causal associations of proteins with risk of T2D and identify potential novel protein drug targets for T2D using conventional and genetic (two-sample Mendelian Randomisation, colocalization test) approaches in multi-ancestry populations;
  • to develop risk prediction models of T2D by incorporating proteomic data in addition to conventional risk factors, and genetic scores for T2D, using conventional and machine learning approaches;
  • to identify potential novel drug targets for T2D using a range of downstream analyses (PheWAS, tissue expression and knockout mouse phenotypes lookup, enrichment analyses, protein-to-protein interaction, etc.);
  • to compare the associations of proteins with T2D and traits in CKB versus UKB and to explore factors contributing to the observed differences.

The student will work within a multi-disciplinary team in CKB. There will be in-house training in systematic literature review, statistical programming, data analysis and scientific writing, and attendance at relevant courses if required. By the end of the DPhil, the student will be competent to plan, undertake and interpret analyses of large and complex datasets, and to report research findings, including publications in peer-reviewed journals as the lead author and presentation at conferences.


Training in advanced statistics, epidemiological methods, molecular epidemiology, programming, and scientific writing will be provided. There may be opportunities to collaborate with industry partners and laboratory research groups. Attendance at seminars, workshops and courses provided by the Department and University will be encouraged. There will be opportunity to present research work at relevant international/national conferences. 

prospective student

Candidates should have a good first degree (2.1) and MSc in epidemiology, statistics, biomedical science, or a related discipline, with a strong interest in population health.