PhD Studentship: Rare variant functional genomics for better understanding, prevention and treatment of musculoskeletal disordersUniversity

Background: Musculoskeletal disorders such as osteoarthritis and Dupuytren’s contracture are severely disabling disorders that affect millions of people in the UK. Genome-wide association studies have identified hundreds of common genetic variants that increase risk of musculoskeletal disorders. Translating these associations into biological insight has, however, been limited because identifying the causal variants is very challenging. Rare variant associations are much simpler to interpret because they have large effect sizes and limited correlation with other genetic variants. In this studentship, the student will analyse millions of individuals across diverse ancestries with whole genome sequencing and medical record data to identify rare genetic variants causing musculoskeletal conditions. They will functionally characterise these variants in relevant tissues to gain mechanistic insights crucial for developing novel, targeted treatments for these disabling disorders.

Key questions and objectives: What are the biological mechanisms that drive the development of musculoskeletal disorders? We will:

1. Identify causal rare genetic variants associated with musculoskeletal disorders using whole genome sequencing from millions of individuals from UK Biobank, All of Us and Our Future Health biobanks

2. Establish the functional consequences of these prioritised variants in disease-relevant, primary human cell and tissue models.

3. Determine the molecular pathways connecting genotype to disease, identifying potential therapeutic targets.

Interdisciplinarity: The student will bridge two leading research environments: health data science and statistical genomics at the University of Exeter, and molecular genetics and cellular modelling at the University of Oxford. This cross-disciplinary approach ensures the student develops a highly sought-after, dual skill set in both computational analysis and experimental validation.

Methodology: The PhD will be divided into two integrated phases.

Phase 1 (Exeter - computational): The student will use large-scale genomic datasets to conduct cutting-edge WGS association testing. Firstly, the student will synthesise information from complex sources, including medical records and assessment data, to define MSK disease phenotypes in UK Biobank, Our Future Health, and All of Us. This will facilitate analysing associated rare variants, structural variants, and integrating multi-omic data (e.g., eQTLs, pQTLs) using advanced bioinformatic pipelines and statistical modelling to prioritise candidate genes and regulatory regions.

Phase 2 (Oxford - laboratory): The student will then functionally validate the top-prioritised candidates. Developed as part of the UKRI HFGI MSK Cluster, the student will interrogate human tissue models of increasing complexity: primary human cells, ex-vivo tissue and humanoid robotic bioreactors. They will employ cutting-edge functional genomics tools - including CRISPR-Cas9 gene editing, transcriptomics, and cellular phenotyping - to define how these variants alter cellular function and cause disease.

Expected outcomes: The project will deliver novel insights into the genetic architecture and molecular drivers of musculoskeletal conditions. Outputs will include the identification of validated causal genes, a deeper understanding of disease pathways, and the potential discovery of new targets for drug repurposing.

Facilities and resources: At Exeter, the student will have access to resources such as UK Biobank, All of Us and Our Future Health as well as high-performance computing infrastructure, and the extensive data science expertise of the supervisors who lead the MSc Health Data Science at Exeter. At Oxford, the student will leverage state-of-the-art molecular biology laboratories, established disease model pipelines, and direct access to well-phenotyped patient tissue biobanks within the Musculoskeletal Cluster.

The studentship funding is for Home tuition fees, as well as a Doctoral Stipend for up to 4 years