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

Overview

This full-time, fixed-term PhD studentship is hosted by the University of Exeter within the Department of Clinical and Biomedical Sciences, and is funded by the EPSRC Engineering and Physical Sciences Research Council. The research project focuses on dissecting metabolic heterogeneity in severe mental illness through genetic clustering and single-cell functional genomics. The overarching goal is to understand why certain individuals with a high genetic liability to conditions such as schizophrenia and bipolar disorder develop obesity and metabolic complications, while others remain metabolically healthy.

What you get

The studentship provides coverage of tuition fees and an annual stipend of £21,805 per annum for the 2026/7 period. The selected candidate will receive comprehensive, multidisciplinary training spanning statistical genetics, genetic epidemiology, bioinformatics, single-cell genomics, and functional genomics. Students gain hands-on experience working with large-scale genomic datasets, causal inference and clustering methods, transcriptomic data integration, and laboratory-based functional studies. Training in Python, R, and high-performance computing is paired with molecular and cellular biology techniques.

Requirements

The project involves identifying genetically distinct clusters of psychiatric risk variants using large-scale genome-wide association study data and assessing their links to obesity and type 2 diabetes. The student will explore the biological and clinical consequences of these clusters across population biobanks and investigate underlying cellular mechanisms using single-cell multiomic datasets. This includes integrating genetic association data with cell-type-specific gene expression and chromatin accessibility data to determine how distinct pathways operate through specific neuronal and glial cell populations. Candidates will also prioritize candidate genes and regulatory elements for functional investigation using techniques such as CRISPR-based perturbation and neuronal cell models.

Key dates

The closing date for applications is 2026-12-08.