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Investigating novel genes and pathways involved in obesity and glycaemic traits related to type 2 diabetes

  • Full or part time
  • Application Deadline
    Monday, December 02, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Lead Supervisor
Prof Inês Barroso, College of Medicine and Health, University of Exeter

Additional Supervisors
Dr Benjamin Housden, College of Medicine and Health, University of Exeter
Prof Tim Frayling, College of Medicine and Health, University of Exeter

Location: University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW

Due to a major recent award, applications are invited from students wishing to further their scientific careers by undertaking a PhD in a diabetes related area of research. Up to four studentships will be fully funded from autumn 2020 with enhanced stipends funded from a new £6 million award. This award reflects Exeter as a world renowned centre of excellence for diabetes research.

Students can select from any of the advertised four projects. These projects have been carefully selected to provide students with an excellent scientific training in an important area of diabetes research, the latest laboratory and computing skills, outstanding resources, and with world leading scientists as supervisors. They cover various aspects of diabetes research, including autoimmunity in the pancreas; neuro-endocrinology to understand the relationship between the brain, mental health and the endocrine system; gene regulation in the placenta and fetal development of the pancreas; rare genetic forms of diabetes; muscle physiology; and the use of electronic medical records to understand disease causes, treatments and progression. Students will learn a wide range of state-of-the-art techniques, which could include CRISPR-Cas9 gene editing, DNA methylation, DNA sequence analysis, muscle insulin sensitivity physiology, brain electrophysiology, medical statistics, R for statistics and data visualisation and programming in python, data science including machine learning, in vivo metabolic phenotype skills and cell biology including 3D stem cell culture. Students will have access to outstanding resources, including cohorts of >5000 patients with rare defects in insulin secretion, a world leading collection of samples for study of pancreas pathology, resources of electronic medical records and biobanks from millions of people and unique resources for studying human development of the pancreas and brain.

Project Description

Large-scale genetic studies of type 2 diabetes, and related traits (e.g. body mass index and glycaemic traits) have identified a number of genetic variants associated with disease risk and normal physiology. However, most of the genetic variants associated with trait or disease risk map outside the coding sequence of genes and so the genes that they affect are largely not known (that is the underlying causal genes are not known). In addition, exome-wide sequencing of childhood obesity cases has identified genes with a high burden of very rare coding variants predicted to adversely affect protein function, in obese children compared to healthy controls. The newly discovered genes influencing childhood obesity have uncharacterized function and unknown interacting protein partners, such that their effect on body weight regulation is not understood.

The aim of this project is to combine existing genetic data with in vivo analysis and genetic interaction screening to both: a. narrow the list of non-coding genetic variants to those most likely to have an effect on function and thus gain further biological understanding of how those variants affect BMI and/or glycaemic traits; b. identify interacting protein partners, and map the newly identified genes influencing childhood obesity with respect to biological pathways. Ultimately knowledge gained may help to identify potential new therapeutic approaches.

This project will enable the student to develop lab based skills, as well as expertise in managing large datasets and use of cutting-edge statistical and computational approaches to analyse results. In addition, there is the opportunity to explore newly identified protein partners (and the genes they encode) for association with a range of additional traits performing phenome-wide association scans. The student will gain experience and training in the use of in vivo Drosophila models, genetic screening using cutting-edge methods, and standard genetic association analysis using large datasets including UK biobank, as well as large-scale consortia genetic data we have access to.

The student should be enthusiastic about experimental and computational analysis, including a desire to learn statistics and genetics – s/he will learn a wide range of data handling and analysis skills, including basic computer script writing, bioinformatics and statistics. The student will join a vibrant, diverse and interdisciplinary research team, including geneticists, mathematicians, computer scientists and clinicians, all studying aspects of diabetes and related conditions.

Funding Notes

This is a 3 year fully-funded PhD studentship. Stipends are at an enhanced rate of £17,059 (2020-21) and all Home/EU tuition fees are covered. Funds will also be available for travel and research costs.

Related Subjects

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