PhD Studentship: (Epi)genetic gene prioritisation

4-year PhD (subject to final funding confirmation): (Epi)genetic gene prioritisation for cerebral small vessel disease

• A first or upper-second class degree (or equivalent) in genetics, genomics, bioinformatics, computational biology, statistics, epidemiology, data science, neuroscience or a related quantitative discipline. A relevant Master's degree is desirable but not essential.
• Essential: programming experience in R and/or Python, and a strong interest in using data to understand human disease.
• Desirable: experience with Unix/HPC environments, statistical genetics (GWAS, QTL, Mendelian randomisation), omics data analysis (methylation arrays, RNA-seq, epigenomics) or large cohort datasets.
• You do not need wet-lab experience.
  
  Start date: October 2027 | Duration: 4 years | Home fee status only
The project Cerebral small vessel disease (SVD) is a major cause of stroke and vascular dementia, and a key contributor to Alzheimer's disease. SVD is highly heritable, but most genetic risk variants lie in non-coding DNA, and how they disrupt gene regulation is largely unknown. This project will build the first integrative map of gene regulatory mechanisms in SVD, linking human genetics to molecular function. The aim is to find new blood biomarkers and genetically supported drug targets. You will lead the analysis of a unique, deeply phenotyped cohort: 147 longitudinally followed SVD patients from the Mild Stroke Study 2 (MSS2). The cohort has genotyping, plasma proteomics, brain imaging, blood-brain barrier measures and 9 years of clinical follow-up. New DNA methylation (EPIC array) and RNA-seq data will be generated for the project. You will compare these with matched controls from the AIRWAVE cohort (~700 samples) and the Lothian Birth Cohort 1936.
• Aim 1 (years 1-2): Find DNA methylation and gene expression changes linked to SVD subtypes, dementia progression, polygenic risk, cardiovascular risk factors and epigenetic ageing.
• Aim 2 (years 3-4): Prioritise causal genes and variants. Methods include meQTL/eQTL analysis, multi-trait analysis with cardiovascular traits and other dementias, colocalisation and Mendelian randomisation. You will integrate the results with histone modification (CUT&Tag), single-cell multiome and proteomic data.
This is a computational (dry-lab) PhD. You will receive training in:
• statistical and population genetics
• epigenomics and multi-omic integration
• causal inference (Mendelian randomisation)
• reproducible analysis on high-performance computing
Training is provided by an interdisciplinary team spanning functional genomics, genetic epidemiology and clinical neuroimaging. Supervisory team
• Dr Alexi Nott, Imperial College London (functional epigenomics and gene prioritisation)
• Prof Joanna Wardlaw, University of Edinburgh (clinical SVD and stroke, MSS2 cohort)
• Dr Axel Montagne, University of Edinburgh (vascular biomarkers, blood-brain barrier)
• Prof Abbas Dehghan, Imperial College London (genetic epidemiology, QTL analysis)
• Dr Fotios Koskeridis, Imperial College London (multi-trait genetics, cardiovascular traits), The studentship covers 4 years of:
• home tuition fees
• a tax free stipend, at least equal to the UKRI London rates (for 2026/27 this is £23,805)
• £5,000 per year for research costs
• £1,000 per year for conference travel
Additional funds can also be applied for to support training and career development.