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MRC Precision Medicine DTP: Combining genetic and functional genomic data to reveal underlying mechanisms of human disease

Project Description

The unprecedented access to genome-wide association (GWAS) signals for a wide variety of human diseases opens up new opportunities to improve understanding of the underlying mechanisms of disease and identify new therapies. We have developed statistical and bioinformatic approaches to collate this information and draw robust inferences about disease mechanisms from public data.1
In our previous work, we demonstrated that many biological pathways can be detected from expression patterns in high-resolution transcriptomic data2, we interrogated shared activity patterns (i.e. coexpression) arising from regulatory regions containing variants associated with inflammatory bowel disease. Our re-analysis of two large GWAS studies reveals two distinct groups of variants associated with both Crohn’s disease and ulcerative colitis. This discovery may indicate two distinct mechanisms underlying each disease. Alternatively, it may indicate the existence of two distinct endotypes3 of each condition. It is at least plausible, and in our opinion probable, that patients with a preponderance of ‘immune’ or ‘epithelial’ genetic variants will respond differently to immunomodulatory therapies.

Aims and training outcomes
The student will develop the computational and statistical tools to detect and validate mechanistic relationships between diseases for which GWAS data are available (340 diseases at the time of writing).
This will form several distinct stages which will overlap in time during the course of the PhD:
1.Detection of mechanistic pathways. Optimisation of coexpression methodology for high-performance computing and incorporation of data from different sources including GTEx, Roadmap Epigenetics and ENCODE.
Training outcomes: process optimisation, parallelisation, SQL database construction and usage, handling large files
2.Development of methodology for evaluation of disease-disease interactions, including linkage disequilibrium score regression, genomic correlation and coexpression analysis.
Training outcomes: statistics, quantitative genetics, regression modelling
3.Application to existing and ongoing GWAS studies. Re-analyses of published and ongoing GWAS studies, including UK biobank, will be performed to detect distinct biological pathways underlying clinical phenotypes. Candidates will be chosen for further validation, in large population studies or clinical trials where genotyping data are available. Biological validation of specific mechanistic hypotheses will be performed in genome-editing experiments collaboration with wet-lab scientists in the Baillie lab (myeloid cells, endothelial cells) and others (hepatocytes, epithelial cells, iPSC-derived primary cells).
Training outcomes: collaboration, hypothesis testing, academic writing
This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location:

Please note, you must apply to one of the projects and you should contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

Funding Notes

Start: September 2019

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualifications, in an appropriate science/technology area.

Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £14,777 (RCUK rate 2018/19) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:


1. Baillie, J. K. et al. Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease. PLOS Computational Biology 14, e1005934 (2018).
2. Forrest, A. R. R., Kawaji, H., Rehli, M., Baillie, J.K., et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).
3. Russell, C. D. & Baillie, J. K. Treatable traits and therapeutic targets: Goals for systems biology in infectious disease. Current Opinion in Systems Biology 2, 139–145 (2017).

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