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Functional genetic variants modify the expression and/or activity of proteins which may represent potential drug targets. These natural experiments in human populations can improve the drug development process, by prioritising targets based on predicted efficacy, assessing safety, and identifying alternative indications.

China Kadoorie Biobank (CKB) is a prospective cohort of 0.5 million participants recruited during 2004-08 ( After ~10-years follow-up, >40,000 deaths and >0.5 million ICD-10 coded hospital episodes were recorded. Genome-wide data, including 80,000 functional genetic variants, are available for 100,000 participants (with the remaining 400,000 expected in the next few years).

Recent work using East-Asian loss-of-function variants in cardiovascular drug targets (e.g. PLA2G7, CETP, PCSK9) demonstrates the value of CKB for assisting drug development (JACC 2016;67:203-231; IJE 2016;45:1588-1599).


This project will use a phenome-wide approach to assess the biological pathways and clinical outcomes associated with genetic variation in potential therapeutic targets, and will identify novel targets through screening and data mining approaches. The project may encompass several areas, including:

  1. Identifying alternative indications for established drugs (i.e. repurposing).
  2. Assessing the efficacy and safety of drug targets at different stages of clinical development.
  3. Screening for novel targets in specified disease areas e.g. cardiovascular; metabolic; neurological; cancer; respiratory; inflammation and immunity.
  4. Identifying the phenotypic and clinical impacts of variations in biological pathways and systems.

There will be training in genetics, epidemiology, bioinformatics and statistics. By the end of the DPhil, the student will be able to plan, undertake and interpret analyses of large-scale genetic and epidemiological data, and report research findings, including 3-5 publications in peer-reviewed journals.


The project will be based in the Big Data Institute Building (BDI), Nuffield Department of Population Health, which has excellent facilities and a world-class community of population health, genetic and data scientists. There will be opportunities to collaborate across scientific disciplines and with the pharmaceutical industry.

prospective candidate

The student should have a 2.1 or higher degree in a biomedical or quantitative science, with a strong background and interest in genetics, statistics and/or computational biology.