About the Project
This interdisciplinary project will involve the student in a close collaboration between three research groups: Schumacher (computational modelling), Chandra (bioinformatics), and Kirschner (stem cell assays)
Age is the single biggest risk factor underlying the onset of many haematological (blood) malignancies. Clonal haematopoiesis of indeterminate potential (CHIP) affects more than 10% of individuals over the age of 60 years and is associated with increased risk for haematological cancers. Little is known about how individual mutations cause clonal outgrowth of blood stem cells and thus contribute to this risk. Knowing the fitness advantage conferred on stem cells by individual CHIP could enable us to predict the risk of progression to leukaemia.
The student will be working with data from the Lothian Birth Cohort, a longitudinal cohort of aged individuals, from which we have deeply sequence targeted chromosomal locations in late life over a 15 year span. The student will quantify variant allele frequency (VAF) for CHIP mutations and use mathematical modelling to infer stem cell fitness in vivo. This will allow us to measure how specific CHIP mutations increase stem cell fitness and reveal how they perturb homeostasis (blood production). The results will enable building of an age-dependent predictor of an individual’s leukaemic risk based solely on their CHIP mutation frequencies. To test these predictions, we have gained access to Generation Scotland data (20,000 participants). The use of mathematical models will allow us to quantitatively combine both longitudinal data on clonal expansion in vivo over 15 years and in vitro data from the Kirschner lab for increased translational impact, improving our understanding of the effects of CHIP on normal haematopoiesis and how this contributes to leukaemia development.
- Developing “T-shaped” skills combining depth in mathematical modelling and bioinformatics with the skill to collaborate and communicate across disciplines (biomedical science, computational biology and cell biology, clinical haematology)
- Bioinformatics, genomics of targeted deep-sequencing data, working with large data-sets whilst maintaining patient anonymity.
- Mathematical modelling using differential equations, stochastic processes, and Bayesian statistics for parameter inference.
- Scientific communication: Communicate complex ideas orally and in writing to both a specialist and lay audience across disciplines.
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. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.
Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.
For more information about Precision Medicine visit:
Qualifications criteria: Applicants applying for an 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 qualification, in an appropriate science/technology area. The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,285 (UKRI rate 2020/21).
Full eligibility details are available: http://www.mrc.ac.uk/skills-careers/studentships/studentship-guidance/student-eligibility-requirements/
Enquiries regarding programme: [Email Address Removed]
Based on your current searches we recommend the following search filters.
Based on your current search criteria we thought you might be interested in these.