PhD Studentship: Dissecting Metabolic Heterogeneity in Severe Mental Illness Through Genetic Clustering and Single-Cell Functional Genomics

This PhD aims to understand why genetic liability to severe mental illness (SMI), particularly schizophrenia and bipolar disorder, is associated with different patterns of adiposity and metabolic health. Although obesity and type 2 diabetes (T2D) are more common in people with SMI, substantial heterogeneity exists, with many individuals remaining metabolically healthy despite a high burden of SMI risk.

We hypothesise distinct components of psychiatric genetic liability influence metabolic health through different biological pathways, with some increasing susceptibility to obesity and T2D while others have little metabolic consequence. The PhD builds on recent work led by supervisor Tyrrell demonstrating that genetic liability to major depressive disorder comprises biologically distinct clusters of variants with contrasting effects on T2D.

Building on this, the student will apply genetic clustering approaches to SMI traits to identify biologically distinct components of psychiatric risk that differentially influence obesity and T2D. By integrating statistical genetics, causal inference approaches and functional genomics, the PhD will move beyond psychiatric diagnoses as single entities to identify the biological mechanisms underlying metabolic heterogeneity.

First, the PhD will identify genetically distinct clusters of psychiatric risk variants using large-scale genome-wide association study data and characterise their associations with obesity and T2D. The biological and clinical consequences of these clusters will be explored across population biobanks. The PhD will then investigate the cellular mechanisms underlying these genetic pathways using unique single-cell multiomic datasets generated in Exeter. By integrating genetic association data with cell-type-specific gene expression and chromatin accessibility data, the student will determine whether metabolically distinct psychiatric risk pathways act through different neuronal and glial cell populations and identify the genes and regulatory networks involved.

Selected regulatory elements and candidate genes emerging from these analyses will be prioritised for functional investigation using experimental approaches (e.g. CRISPR-based perturbation, neuronal cell models). This will provide mechanistic insight into how distinct components of SMI genetic liability influence both brain function and metabolic health. The PhD will provide multidisciplinary training in statistical genetics, genetic epidemiology, bioinformatics, single-cell genomics and functional genomics.

The student will gain experience in large-scale genomic datasets, causal inference and clustering methods, integrating transcriptomic data, and undertaking lab-based functional studies. Training in R, Python and high-performance computing will be complemented by experimental skills in molecular and cellular biology. While the PhD has clearly defined objectives, there will be considerable scope for the student to shape the direction of the PhD according to their interests and emerging findings.

Opportunities may include focusing on a specific SMI, ancestry-specific mechanisms, method development, particular neuronal cell populations, or in-depth functional characterisation of candidate genes. This flexibility will support the development of an independent research profile. This PhD will address a key question in psychiatric and metabolic disease biology: why do some individuals with a high SMI genetic liability develop obesity and metabolic complications whilst others remain metabolically healthy?

By identifying biologically distinct components of SMI risk and characterising the molecular mechanisms, the PhD has the potential to reveal new pathways linking brain function and metabolic regulation, improve understanding of disease heterogeneity, and identify novel targets. It will equip the student with a broad, highly transferable skills relevant to careers in academia, biotechnology and precision medicine.

Funded by: EPSRC Engineering and Physical Sciences Research Council