Developing a framework for precision medicine in a complex trait

Diabetes currently affects 415 million people worldwide. In the UK, there will be 5M people with type 2 diabetes (T2D) by 2025, accounting for 1 in 30 prescriptions and £25 billion in annual NHS costs. Prevalence rates are even higher in many other parts of the world: in some societies, adults with normal glucose levels are in the minority.

The focus on delivery of personalised (or precision) medicine reflects the expectation that developments in genomics, imaging etc will extend our diagnostic and prognostic capabilities, and enable more effective targeting of preventative and therapeutic options. The benefits of this approach are being realised in rare diseases and cancer but impact on management of complex diseases, such as type 2 diabetes, remains limited. This seems likely to reflect reliance on inappropriate models of disease architecture, based around rare, high-impact genetic and environmental exposures poorly suited to our emerging understanding of T2D.

We have recently proposed an alternative ‘palette’ model, centred on a molecular taxonomy that focuses on considering T2D-risk in terms of individual configurations with respect to the major pathophysiological processes that contribute to diabetes risk and progression. This model fits well with the genetic and genomic data on T2D predisposition that our group has gathered. It anticipates that many individuals with diabetes will have suboptimal performance across several pathophysiological processes.

Under such a model, the key steps required for translational advances in T2D are

Enumeration of the key pathophysiological processes (using genetics, human physiology, cellular & animal models etc);
Defining biomarkers that provide readouts of each of these processes;
Mapping these processes onto “real world” data from large biobanks and health care datasets.
This DPhil project will involve a range of complementary approaches to tackle one of more of these challenges, making use of the extensive data sets and resources available to the McCarthy group. Possible projects might include

Mapping T2D-risk variants onto cellular, molecular, physiological and epidemiological data sets;
Using 2-sample MR methods to identify putative biomarkers of T2D risk, progression and phenotype (using access to large multiomic data sets we are generating through DIRECT and MultiMUTHER consortia);
Mining UK biobank and other similar data sets to which we have collaborative access, to identify phenotypic clusters and map these to genetic and other data.
Precise project details will depend on the interests and skills of the student, and the status of this work as of October 2018. This work is funded by the Wellcome Trust and the US National Institutes of Health.

TRAINING OPPORTUNITIES

The DPhil would be based primarily at the Wellcome Trust Centre for Human Genetics but with strong interactions with colleagues at the Oxford Centre for Diabetes Endocrinology and Metabolism and the Big Data Institute. The student will receive training in diverse aspects of complex trait genetics, and will benefit from the strong computational and statistical focus of the WTCHG, BDI and other collaborating groups. The student will also have the opportunity, through existing collaborations, to interact with other world-leading groups active in human genetics, large-scale epidemiology and the development and implementation of relevant statistical methodologies. Through the strong network of diabetes collaborators in Oxford and beyond, the student will be well-placed to further develop their understanding of related biology. The core of the project is computational and statistical and the student will deploy and develop their skills in the management of complex large biomedical and genomic data sets. Depending on interest and aptitude, the student will have the possibility to pursue follow-up of the findings that emerge in a variety of alternative directions, through a focus on the application of in silico methods, the generation of additional genomic of functional data, or the extension of findings to additional data sets. This project provides an opportunity for a highly-motivated student with strong computational and analytical skills, and an interest in global aspects of human health and biology, to train in one of the internationally-leading centres at a uniquely-exciting time in the development of human genetics.

As well as the specific training detailed above, students will have access to a wide-range of seminars and training opportunities through the many research institutes and centres based in Oxford. Generic skills training is offered through the Medical Sciences Division’s Skills Training Programme. The Department has a successful mentoring scheme, open to graduate students. We hold an Athena SWAN Silver Award in recognition of our efforts to support the careers of female students and staff.