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Use of pharmacological treatments for blood pressure lowering in clinical practice is guided by results of randomised controlled trials (RCTs). However, most RCTs last only a few years and are restricted to high-risk or diseased individuals, or individuals with high levels of blood pressure. Hence, recommendations on use of such treatments may involve extrapolation of the available evidence from a few common diseases, with substantial uncertainty about their long-term benefits and hazards in diverse populations. Mendelian randomisation (MR), involving use of genetic variants as proxies for classes of blood pressure-lowering medication, affords an alternative approach to assess the efficacy and safety of lowering blood pressure on disease outcomes. The analyses will involve participants from two contemporary prospective studies in the UK and China (UK Biobank and China Kadoorie Biobank) with detailed genetic data linked to a wide range of disease outcomes in diverse populations.


This DPhil will utilise data from the established China Kadoorie and UK Biobanks and will investigate:

  • Associations of genetic variants within genes encoding drug targets for widely used pharmacological agents for lowering blood pressure with major vascular diseases (e.g. stroke and coronary heart disease); 
  • Associations of such variants with a wide range of other disease outcomes (phenome-wide approaches) to examine possible adverse effects and potential for repurposing such treatments. 
  • Modelling the predicted effects of treatment in order to guide the design of future blood pressure-lowering trials. 

There will be in-house training in epidemiology, statistics, and genetics, and attendance at relevant training courses such as the Wellcome Trust course “Genetic Analysis of Population-based Association Studies”. By the end of the DPhil, the student will be competent to plan, undertake and interpret analyses of large-scale genetic data using state-of-art MR techniques, and to report research findings, including some publications as the lead author in peer-reviewed journals. The student will also gain experience in conducting systematic literature reviews, academic writing, data management and presentation of study findings at national/international conferences.


The student will work within a multi-disciplinary team within the CKB research group, part of the Nuffield Department of Population Health and based in the Big Data Institute. There are excellent facilities and a world-class community of population health, data science and genomic medicine researchers. There may be opportunities to work with external partners from industry and other research institutions. 


A good first degree in a quantitative discipline is desirable.

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