Using genetics to improve the prediction of large for gestational age babies in women with gestational diabetes - PhD Medical Studies (Funded)

The University of Exeter’s College of Medicine and Health is inviting applications for a fully-funded PhD studentship to commence in October 2019 or as soon as possible thereafter. For eligible students the studentship will cover UK/EU tuition fees plus an annual tax-free stipend of at least £17,000 for 3 years full-time, or pro rata for part-time study. The student would be based in the RILD Building at the Royal Devon and Exeter Hospital Campus, College of Medicine and Health, Exeter.

This award provides annual funding to cover UK/EU tuition fees and a tax-free stipend. For students who pay UK/EU tuition fees the award will cover the tuition fees in full, plus a tax-free stipend (£17,000 year 1, £17,500 year 2, £18,000 year 3), and a training/travel budget of £3,300 per annum. Students who pay international tuition fees are eligible to apply, but should note that the award will only provide payment for part of the international tuition fee and no stipend.

Dr. Freathy leads internationally-renowned research that specialises in using large-scale human datasets to identify genetic and non-genetic influences on fetal growth. Dr. Shields has pioneered combined prediction modelling of clinical and biomarker data to improve the diagnosis of diabetes, and Prof. Hattersley is distinguished for his ground-breaking contributions to the understanding of the genetics of diabetes and its application to clinical practice.

The main aim of this research project is to use genetic information and other factors to develop a risk prediction tool for large for gestational age (LGA) babies, to help prioritise care for women with diabetes in pregnancy. Women with diabetes in pregnancy are at a higher risk of their baby growing larger than usual. This can make it more likely that induced labour or a caesarean section will be needed, or that the baby will require extra medical care. However, not all women with diabetes in pregnancy have large babies. Many factors other than the mother’s blood glucose levels also influence how large the baby will grow. Improved prediction of pregnancies at greatest risk in the early stages of pregnancy, before detection is possible by ultrasound scan, is important as it would help identify patients in need of intensive follow-up. The student will develop clinical prediction models using data from the Born in Bradford study, and will validate these in four other world-leading datasets (EFSOCH, Atlantic DIP, ALSPAC, HAPO). The overall scientific goal is to build a model containing a set of predictors (including genetic scores) that will improve early prediction of LGA relative to information currently routinely used. Ultimately, this could lead to therapeutic changes being made earlier to enhance prevention of adverse outcomes.

This studentship offers internationally-excellent training in statistics and statistical genetics, bioinformatics and clinical prediction modelling. The student will have hands-on experience of several world-leading datasets and will be part of a supportive, dynamic and successful team of researchers. They will also perform a pilot lab-based study to test the feasibility of non-invasive genotyping of birth weight-associated variants from cell-free fetal DNA in the maternal circulation. At the end of the PhD they will have an excellent basis for a career combining statistics and data skills with clinically relevant research.

The student will be part of the larger multidisciplinary Complex Traits Genetics Team and clinical diabetes research team at the College of Medicine and Health. The teams regularly publish high impact papers in journals such as Nature Genetics, Diabetes, JAMA, The Lancet and Nature. The student will benefit from full involvement in multidisciplinary discussions, seminars and workshops, which will enhance their communication skills and provide feedback on their own project. They will have several opportunities to attend and present their work at relevant scientific conferences.

Please contact Dr Freathy informally for more details on +44 1392 408238.