Phenome-wide association studies of functional genetic variants to validate drug targets (MRC PHRU)

This project will be supported with an MRC PHRU Studentship if there is a suitable candidate.

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, assisting in prioritising of targets based on predicted efficacy, assessing safety, and identifying alternative indications. Large prospective biobank studies with detailed characterisation of large numbers of apparently healthy individuals from the general population, using conventional and novel technologies, and with electronic monitoring of a wide range of health conditions are uniquely positioned to fulfill these goals.

The China Kadoorie Biobank (CKB) is a prospective cohort of 0.5 million participants established during 2004-08, with follow-up through linkage to death registries and hospital records (www.ckbiobank.org). To date, there are ~40,000 deaths and ~0.9 million ICD-10 coded episodes of hospitalisation of >1300 different disease types (e.g. stroke, heart disease, cancer, diabetes, fracture, dementia, cataract and rheumatoid arthritis) recorded among CKB participants. Genome-wide SNP data, including 80,000 potential functional genetic variants, are available for 100,000 participants (with the remaining 400,000 expected in the next 2-3 years). These data are complemented by blood biomarkers (e.g. metabolomics, proteomics, infections serology).

Recent work in CKB using East-Asian specific loss-of-function variants in genes encoding cardiovascular drug targets (e.g. PLA2G7, CETP, PCSK9) demonstrates the value of large prospective biobanks for assisting drug development (JACC 2016 67:203-231; IJE 2016 45:1588-1599; JAMA Cardiol 2018 3:34-43).

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

This DPhil 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 for certain diseases through screening and data mining approaches. The project may encompass several areas of work, including:

1. Identifying alternative indications for established drugs (i.e. repurposing).
2. Assessing 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 opportunities in genetics, epidemiology, bioinformatics and statistical analysis including attendance at relevant courses (eg, the Wellcome Trust course “Genetic Analysis of Population-based Association Studies”). 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 to report research findings, including publication and presentation at national/international conferences.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

The project will be based at CTSU, Nuffield Department of Population Health, in the Big Data Institute (BDI) building, 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, depending on the direction of the project.

Prospective candidate

The candidate should have a 2.1 or higher degree in a biomedical or quantitative science or medicine, with a strong interest in epidemiology, genetics, or statistics. The project will involve large-scale data and statistical analyses and, therefore, requires some previous statistical and programming training/experience and an aptitude and interest in extending these skills.