About the Project
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 five studentships will be fully funded from autumn 2020 with enhanced stipends funded from a new £6million award. This award reflects Exeter as a world renowned centre of excellence for diabetes research.
Students can select from any of the advertised five 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.
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.
Project Description
This studentship will investigate non-coding sequence variants using genetic and genomic datasets to understand their causative role in disease. The student will develop their knowledge of transcription factor binding and chromatin biology in addition to receiving training in data science and machine learning.
Genetic studies have significantly improved our understanding of how sequence variation impacts disease. Progress thus far has centred on the role of common variants and rare mutations in sequences that code for genes, however many disease-associated sequence variants fall in non-coding regions which act to precisely control gene expression in health and disease. The impact of changes in non-coding sequences on disease ranges from variants that modify the risk of developing a particular disease to rare mutations that are causative of a monogenic disorder. Currently, we lack a base-pair level understanding of non-coding regions and so are unable to accurately predict how sequence changes impact gene regulation, rendering the study of non-coding variants more challenging than their coding counterparts. However, recent advances in genomic technologies that measure epigenomic states have significantly improved our understanding of the mechanisms of gene regulation and offered us tools to investigate the impact of sequence changes in non-coding regions.
This studentship will apply data science and machine learning tools to genetic and genomic datasets to predict the impact of non-coding variants in disease. The student will investigate a catalogue of non-coding variants previously reported in diabetes and will focus on understanding how sequence changes impact transcription factor binding and local chromatin states. A goal will be to build predictive sequence models of gene regulation that will attribute causality to variants in disease.
The project will allow the student to develop expertise in genetic and genomic data, and particularly chromatin biology and machine learning. The student should be interested in genomics and gene regulation and comfortable with data analysis and statistical methods. The student will join an interdisciplinary research team allowing them to develop a broad range of knowledge and skills in the fields of diabetes, gene regulation, genomics, and data science.
Funding Notes
Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK. Applicants with a Lower Second Class degree will be considered if they also have Master’s degree. Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.
Applicants must ensure that they meet the eligibility requirements of the University of Exeter. To qualify for ‘home’ tuition fee status, you must be a UK or EU citizen who has been resident for 3 years prior to commencement.
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