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BACKGROUND

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, such as assisting in prioritising targets based on predicted efficacy, assessing safety, identifying alternative indications and informing clinical trial designs. Large prospective biobank studies, such as China Kadoorie Biobank (CKB) and UK Biobank (UKB) are uniquely positioned to fulfil these goals.

In CKB and UKB, electronic health record linkage records deaths and hospitalisation episodes for thousands of different diseases. Genome-wide data are currently available for 100,000 CKB participants, with genome-wide and whole genome sequence data in all UKB participants. Proteomic data for 10,000 proteins in 4,000 CKB participants, and for 3,000 proteins in 50,000 UKB participants, are available. These are complemented by blood biomarkers (e.g. clinical biochemistry, metabolomics, serology) in subsets of participants. Previous research highlights the benefits of assessing drug targets using genetic data from diverse populations. 

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The DPhil project will assess the biological pathways and clinical outcomes associated with genetic variation in potential therapeutic targets, and will identify novel protein targets for certain diseases. The specific area of research will be developed according to the student’s interests and aptitude, and may include the following key objectives: 

  1. assessing efficacy, safety, and alternative indications (i.e. repurposing) of established and emerging drug targets at different stages of clinical development, using relevant functional genetic variants; 
  2. identifying novel targets through causal associations of proteins with specific diseases or disease areas e.g. cardiovascular, neurological, and cancer, using pQTLs in a Mendelian randomisation approach;
  3. exploring the phenotypic and clinical impacts of variations in biological pathways and systems, using a range of biomarkers, in CKB, UKB, and other available datasets.

The student will work within a multi-disciplinary team. There will be training in genetics, epidemiology, statistical analysis, and attendance at relevant courses if required. By the end of the DPhil, the student will be able to plan, undertake and interpret analyses of large-scale genetic, proteomic, and epidemiological data, and report research findings, including conference presentation and publications as the lead author in peer-reviewed journals.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

The project will be based within the CKB research group, 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. 

PROSPECTIVE  STUDENT

The ideal candidate will have good first degree (2.1 or higher) and MSc in a relevant subject, with a strong interest in epidemiology, genetics, or statistics. The project will involve large-scale data analyses and requires previous statistical and programming experience.

Supervisors