Integrative genomics prioritisation of drug targets

We are offering an exciting opportunity to carry out a PhD that will use cutting-edge genetic and bioinformatic methods to identify drug targets, potential side effects and repurposing opportunities. Your research could lead to changes in the treatment or prevention of disease, and will provide you with an excellent foundation for a career in translational biomedical research. You would be based in the Data Mining programme, part of the MRC Integrative Epidemiology Unit at the University of Bristol, an internationally-leading leading centre for the development and application of causal analysis and evidence triangulation methods. You will be supported by an interdisciplinary team of academic staff who are experts in their fields, and join a diverse cohort of students working across many different areas of health sciences. For more information about the MRC Integrative Epidemiology Unit and the PhD programme, please visit the website.

Rationale

Genetic evidence has been retrospectively shown to increase the odds of a success of given drug target (1), and offers a lot of potential to decrease the costs of drug development and identify drug repurposing opportunities. However, despite the abundance of genome-wide association studies (GWAS) for almost all common complex diseases, researchers still face a lot of difficulty in how to interpret these findings to best identify drug targets (2). Integrating the evidence from common genetic variants (from GWAS) with other sources of genetic and molecular evidence can aid this process and help strengthen the evidence for valid drug targets.

In this project, you will apply cutting-edge data science methods to the growing wealth of genetic and bioinformatic data resources to help better prioritise drug targets for a variety of human diseases. In particular, you will use data bridging the gap between common disease variants and rare Mendelian disease mutations with matching underlying molecular targets and symptoms (3).

Aims and objectives

• investigate how the use of rare mutations can complement the evidence from common, less deleterious, mutations targeting the same genes towards drug target prioritisation
• map text descriptions of disease phenotypes to identify Mendelian disease variants that have similar phenotypic effects to those of common molecular quantitative trait loci (QTL) controlling transcript expression, protein abundance, methylation etc. (4)
• integrate population-level molecular QTL evidence from with evidence from rare exome mutations (AstraZeneca PheWAS Portal, Genebass), Mendelian disease mutations (OMIM, ClinVar, Born in Bradford) and mouse knockouts (Mouse Genome Informatics database)
• utilize molecular pathway and protein-protein interaction data to provide supporting evidence of functional relevance from related proteins

Methods

You will apply a variety of genetic epidemiology methods to predict the effects of drug targets, including Mendelian Randomization (MR) (5), fine mapping and genetic colocalization. MR uses genetic polymorphisms, such as obtained in GWAS and QTL mapping studies, as instruments to help establish risk/protective factors for common human disease.

You will also learn how to deploy Natural Language Processing (NLP) techniques to map common disease phenotypes to rare Mendelian disorders, and could explore the potential of large language models (LLMs) to identify evidence from the biomedical literature.

You will also use a range of biomedical databases used in drug target prioritisation (e.g. DrugBank, Open Targets, Chembl), and use innovative approaches to integration of these data with genetic epidemiological and literature-based evidence.

Candidate requirements:

We strongly encourage applications from a range of disciplines (e.g., mathematics, statistics, computer science, life or natural sciences, psychology, social sciences or other related quantitative discipline). Applications are sought from high performing individuals who have, or are expected to obtain, a 2.1 or higher degree (or equivalent). Possession of a relevant Master's degree or research experience would be advantageous but is not expected.

How to apply

When applying, candidates must select the Population Health PhD programme and enter supervisor names as listed under the project title for which they are applying. Please state IEU funding in the funding box. Full details on what to include in your application can be found in the Admissions Statement.

Personal statement: Please also provide a personal statement that describes your training and experience so far, your motivation for doing a PhD, your motivations for applying to the University of Bristol, and why you think we should select you. We are keen to support applicants from minority and under-represented backgrounds (based on protected characteristics) and those who have experienced other challenges or disadvantages. We encourage you to use your personal statement to ensure we can take these factors into account. 

University of Bristol, Bristol Medical School

Bristol Medical School is the largest and one of the most diverse Schools in the University of Bristol, with approximately 1100 members of staff, 1350 undergraduate, 250 postgraduate taught and 240 postgraduate doctoral research students. The Head of School is Professor Chrissy Hammond. The Medical School has two departments: Population Health Sciences and Translational Health Sciences. The School is a leading centre for research and teaching across these areas. Research in the School is collaborative and multi-disciplinary, with staff coming from a wide range of academic disciplines and clinical specialties.

The 2021 Research Excellence Framework (REF) confirmed the University of Bristol’s position as a leading centre for health research. Bristol Medical School contributed to three Units of Assessment including UoA1 (Clinical Medicine), UoA2 (Public Health, Health Services and Primary Care) and UoA4 (Psychology, Psychiatry and Neuroscience). The UoA2 submission, comprising predominantly Medical School staff. was ranked 3rd in the UK with 94% of our submitted research outputs rated as world leading (4*) or internationally excellent (3*). Submissions to UoA1 and UoA4 were shared with varying degrees of representation with the Faculty of Life Sciences. Respectively UoA1 and UoA4 had 94% and 84% of submitted research ranked as 4* or 3*, which represented increases in each category in the proportions of 4* ranked papers as well in growth in GPA rankings above the previous REF2014.

The School is committed to delivering a positive working environment for all staff, it holds Silver Athena SWAN Awards in recognition of the ongoing commitment to promote equality, diversity and inclusion within the School.