PhD Studentship: Functional genomics of health and unhealthy fat distributionUniversity

Where fat is stored in our bodies is a major determinant of health. Storing fat safely under the skin (subcutaneous fat) protects against metabolic disease like heart failure and type 2 diabetes, while storing fat centrally around the organs (visceral fat or "lipodystrophy") drastically increases disease risk. Although human genetics has identified some protein-coding genes that substantially affect fat distribution, the vast majority of disease-associated variation is hidden in non-coding regulatory regions that have been difficult to access and interpret at scale. Now, with increasing availability of whole-genome sequencing from millions of individuals; fat-specific genetic regulatory maps; AI variant interpretation tools, and advances in functional models of fat cells, we are in prime position to effectively interrogate these regions. This will allow us to determine which variants and genes affect fat distribution.

Key Question and Objectives:

Which genetic regulatory switches and genes drive human visceral versus subcutaneous adipocyte identity, and what are the biological mechanisms?

The student will:

1) Identify coding and non-coding variants associated with fat distribution using whole genome sequencing data across massive multi-ancestry cohorts.

2) Integrate genetic signals with multi-omic epigenetic maps, including AlphaGenome AI predictions, to prioritise and interrogate causal regulatory elements.

3) Establish human iPSC-derived adipocyte models patterned toward visceral and subcutaneous adipocytes.

4) Quantify the functional impact of prioritized genes and regulatory variants on adipocyte phenotypes using CRISPR-based perturbations.

Interdisciplinarity: This project is interdisciplinary, bridging data science with experimental biology. The studentship will comprise world-leading computational genomics and population genetics at the University of Exeter, alongside cutting-edge stem-cell biology and CRISPR-Cas9 genome editing at the University of Cambridge. The supervisors also include leading senior clinical academics with expertise in lipodystrophy providing important clinical insight for the student.

Methodology:

1) At Exeter, you will harness established population-genetic association pipelines to analyse WGS and fat measurements from highly diverse cohorts. You will integrate these genetic signals with adipose-specific regulatory information (including gene expression, chromatin accessibility, enhancer gene relationships) and AI-based tools such as AlphaGenome to prioritise and test a set of candidate variants, regulatory elements and target genes.

2) At Cambridge, you will develop iPSCs patterned towards visceral and subcutaneous adipocyte identity, guided by developmental cues. A stable Cas9-expressing iPSC line will establish a reproducible perturbation pipeline, benchmarked using established adiposity genes like ALK7/ACVR1C and PLIN1. You will use high-content imaging and biochemical assays will quantify adipogenesis, lipid storage, and lipolysis. Your validated functional framework will then run a primary CRISPR knockout screen of genetically prioritised candidates.

Expected Outcomes:

The studentship will deliver a validated platform of human iPSC-adipocytes and uncover the biological mechanisms behind non-coding variants controlling human fat distribution. This will identify new therapeutic drug targets for cardiometabolic disease that focus on safe fat storage rather than just overall weight loss.

Facilities and Resources:

At Exeter, you will use large-scale computational platforms and Cloud Trusted Research Environments (DNANexus) to analyse over 1 million genomes. At Cambridge, you will be embedded in world-leading metabolic science facilities with comprehensive access to advanced iPSC infrastructure, high-content imaging, and flow cytometry.

Funded by: EPSRC Engineering and Physical Sciences Research Council

£21,805. The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7.