PhD Studentship: Dissecting Metabolic Heterogeneity in Severe Mental Illness Through Genetic Clustering and Single-Cell Functional Genomics University of Exeter in United Kingdom

[Funding of fees plus a stipend of £21,805 per annum for 2026/7.]

PhD Studentship at the University of Exeter on Dissecting Metabolic Heterogeneity in Severe Mental Illness Through Genetic Clustering and Single-Cell Functional Genomics.

This project aims to understand why genetic liability to severe mental illness, especially schizophrenia and bipolar disorder, is linked to different patterns of adiposity and metabolic health. While obesity and type 2 diabetes are more common in people with severe mental illness, many individuals remain metabolically healthy despite high psychiatric genetic risk. The PhD will investigate whether distinct components of psychiatric genetic liability influence metabolic outcomes through different biological pathways.

The student will build on recent work showing that genetic liability to major depressive disorder can be divided into biologically distinct clusters with contrasting effects on type 2 diabetes. Using large-scale genome-wide association study data, the project will identify genetically distinct clusters of psychiatric risk variants and test how they associate with obesity and type 2 diabetes. The work will extend into population biobanks to examine biological and clinical consequences across cohorts.

A major part of the PhD will use unique single-cell multiomic datasets generated at Exeter. By integrating genetic association results with cell-type-specific gene expression and chromatin accessibility data, the student will study whether metabolically distinct psychiatric risk pathways act through different neuronal and glial cell populations, and will identify the genes and regulatory networks involved. Candidate regulatory elements and genes may then be taken forward for functional testing using approaches such as CRISPR-based perturbation and neuronal cell models.

The project offers multidisciplinary training in statistical genetics, genetic epidemiology, bioinformatics, single-cell genomics, functional genomics, causal inference, and clustering methods. The student will also gain experience with R, Python, high-performance computing, transcriptomic data integration, and laboratory-based functional studies. There is flexibility to shape the project direction, with possible emphasis on a particular severe mental illness, ancestry-specific mechanisms, method development, specific neuronal populations, or deeper functional characterisation of candidate genes.

This is a funded PhD studentship supported by the EPSRC Engineering and Physical Sciences Research Council. The award provides fees and a stipend of £21,805 per annum for 2026/7. The application deadline is 2026-12-08.