Genetic approaches to assess and optimise drug targets
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), with electronic monitoring of a wide range of health conditions are uniquely positioned to fulfil these goals (www.ckbiobank.org/research/drug-targets).
In CKB, follow-up through linkage to death registries and hospital records has already recorded >70,000 deaths and >1.5 million episodes of hospitalisation for >5000 different disease types. Genome-wide SNP data, including 80,000 potential functional genetic variants, are currently available for 100,000 participants. These data are complemented by whole genome sequence data and blood biomarkers (e.g. metabolomics, proteomics and serology) in a subset of participants. Previous research highlights the benefits of assessing drug targets using genetic data from diverse populations (www.ckbiobank.org/achievements/drug-targets).
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 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:
- Identifying alternative indications for established drugs (i.e. repurposing);
- Assessing efficacy and safety of drug targets at different stages of clinical development;
- Screening for novel targets in specified disease areas e.g. cardiovascular, metabolic, neurological, and cancer;
- Identifying the phenotypic and clinical impacts of variations in biological pathways and systems.
The student will work within a multi-disciplinary team. There will be training opportunities 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 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, 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.
Candidates should have a 2.1 or higher degree 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.