Genetics and Genomics of Type 2 Diabetes: using human genetics to drive a mechanistic understanding of type 2 diabetes and to identify novel translational opportunities
Prof M I McCarthy
Dr A Gloyn
No more applications being accepted
Self-Funded PhD Students Only
We focus on the use of human genetics to drive a mechanistic understanding of type 2 diabetes, and to identify novel translational opportunities.
We combine experimental and computational strategies, assembling diverse types of large-scale genetic genomic, molecular and clinical data, and deploying a wide range of statistical and computational approaches to mine them. We have a strong track record of providing postgraduate training in the analysis and interpretation of large-scale biomedical data sets, and several of our students have taken on projects that combine experimental and computational components. We interact closely with the research group led by Professor Anna Gloyn, and many of our students have been jointly supervised.
The foundation of our group’s work lies in the identification of DNA sequence variants influencing risk of T2D and related traits. I lead international consortia that have used large-scale genome wide association (~1.5M people) and exome sequencing analyses (~50,000) to uncover over 400 T2D-association signals: further expansion of these efforts is planned. We integrate genomic information from diabetes-relevant tissues (islet, fat, muscle) to understand the molecular and cellular impact of these variants, and to identify the effector transcripts through which they mediate their effects. In collaboration with Prof Gloyn, we seek to functionally validate these findings, using diverse techniques, including genome-editing and high-throughput screens. We aim to identify the shared pathways and networks through which multiple association signals operate.
A major focus of our activities lies in the development of partitioned polygenic risk scores: these set out to deconstruct T2D genetic risk into components mediated through particular pathophysiological defects (such as defective insulin secretion, insulin resistance or adiposity). These partitioned risk scores allow us to capture the clinical and phenotypic heterogeneity within T2D, and to determine whether these offer clinical utility with respect to the prediction of relevant clinical outcomes (such as drug response and complication risk).
Students within the lab will receive training in the analysis and interpretation of large biomedical data sets including (dependent on their decided project) genome-wide association analysis, exome sequencing, whole genome sequencing, NGS readouts of regulatory function (eg ATAC-Seq), RNA-Seq, network analysis and protein-protein interaction data. There are also opportunities to be trained in relevant experimental techniques (again dependent on the project) including cell-culture, genome-editing, stem-cell differentiation, single-cell analysis and high throughput genetic screens
As well as the specific training detailed above, students will have access to high-quality training in scientific and generic skills, as well as access to a wide-range of seminars and training opportunities through the many research institutes and centres based in Oxford.
The Department has a successful mentoring scheme, open to graduate students, which provides an additional possible channel for personal and professional development outside the regular supervisory framework. We hold an Athena SWAN Silver Award in recognition of our efforts to build a happy and rewarding environment where all staff and students are supported to achieve their full potential.
Our main deadline for applications for funded places has now passed. Supervisors may still be able to consider applications from students who have alternative means of funding (for example, charitable funding, clinical fellows or applicants with funding from a foreign government or equivalent). Prospective applicants are strongly advised to contact their prospective supervisor in advance of making an application.
Please note that any applications received after the main funding deadline will not be assessed until all applications that were received by the deadline have been processed. This may affect supervisor availability.
Nature Genetics 2018;50:559-571. PMID: 29632382; PMCID: PMC5898373.
Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci bioRxiv 190892; doi: https://doi.org/10.1101/190892. Elife 2018;7:e31977. PMID: 29412141 PMCID: PMC5828664
The genetic architecture of type 2 diabetes. Nature. 2016;536:41-47. PMID: 27398621. PMCID: PMC5034897
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