PhD: (Epi)genetic Gene Prioritisation for Small Vessel Disease

Imperial College London and the UK Dementia Research Institute seek a computational PhD student to build the first integrative map of gene regulation in cerebral small vessel disease, on a funded four-year studentship with Dr Alexi Nott.

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

Department of Brain Sciences, Imperial College London and the UK Dementia Research Institute

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)

Who we’re looking for

• 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.

Funding

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.

Eligibility: home fee status only. The studentship covers home-rate tuition fees only, so we can only consider applicants who qualify for home fee status. Please check your fee status with Imperial’s fee status guidance before applying.

How to apply

Email Dr Alexi Nott (a.nott@imperial.ac.uk) with “epiSVD PhD” in the subject line, and attach:

• A CV, including any coding or data-analysis experience
• A cover letter (max 1 page) explaining your interest in the project and how your skills fit it
• Contact details for two referees

Please confirm your fee status in your cover letter. Informal enquiries are welcome; please use the same subject line.

Applications will be reviewed as they are received, and the advert will close once a suitable candidate is identified, so early applications are encouraged. Shortlisted candidates will be interviewed and supported through the funder’s formal application process.

Funding Notes

This PhD position includes a tax-free stipend (for guidance, the UKRI London rate 2026/27 is £23,805) and UK/Home fees for 3.5 years. The stipend may be extended for up to six months during the writing up period, if required.

Qualification for Home fees is only available to UK citizens or those who have been resident in the UK or a period of 3 or more years. More detail is provided in UKCISA’s guide View Website

Applicants are also required to meet Imperial’s English language requirements View Website