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external supervisor

Joanna Howson, Novo Nordisk


Diabetes mellitus is a leading cause of morbidity and mortality worldwide, and is associated with microvascular and macrovascular complications. The recent explosion in the availability of large-scale genetic data can help to offer insights into the determinants of diabetes and related complications, and provides an unprecedented opportunity to integrate biomedical and genetic information to uncover disease mechanisms and identifying therapeutic targets.

This project will use data from 0.5 million UK Biobank participants for undertaking Mendelian randomisation analyses to explore causal mechanisms related to the diabetic disease process and related complications. You will explore potentially causal associations between established and novel biomarkers and vascular complications of diabetes and their mediating pathways, and will consider potential heterogeneity in these relationships. The work will capitalise on the use of genetic data, electronic healthcare records, and blood biomarkers, as well as make use of novel information currently being derived from imaging data, to develop insights into potential therapeutic pathways and drug re-purposing.

This project represents an academic collaboration between NDPH and Novo Nordisk scientists, and thereby offers a unique opportunity to gain insights from multiple perspectives. Moreover, it may be possible to capitalise on novel relevant phenotypes being generated through a collaboration between different research groups that focuses on using deep learning approaches to identify features from imaging data.


You will be based primarily in the Hopewell Group, at the Big Data Institute, among a team of specialists in genetic epidemiology and large-scale studies, and learn from a multidisciplinary team of scientists from across academia and industry. You will gain experience of (and training in) genetic epidemiology, Mendelian randomization analysis and handling big data, as well as of diabetes and vascular disease, and more general population health research skills.


This academic collaboration between NDPH and Novo Nordisk scientists offers unique opportunities to gain insights from academic and industry perspectives (as represented by the supervisory team). 

Prospective candidate

The project involves large-scale analysis of big data from UK Biobank, and requires some previous statistical background and programming experience (e.g. R, SAS) and an interest in developing these skills further. Examples of suitable prior qualifications are an MSc in Medical Statistics/Genetic epidemiology or related field with a strong statistical/programming component.