UCL Cancer Institute


Medical Genomics Group

The Medical Genomics Group has broad interests in the genomics and epigenomics of phenotypic plasticity in health and disease. We use computational and data science approaches to study genetic and epigenetic variations and how they modulate genome function. Our research aims to advance translational, regenerative and personalized medicine. We also advocate for more open data sharing and governance and science in general.

Research Projects

Methylome Analysis

DNA methylation is involed in many biological processes, health states and treatment options. The aim of this project is to develop novel computational methods for advanced methylome analysis in health and disease. Current developments include new tools for RRBS (CAMDAC), imputation (GIMMEcpg), handling of large data tables (MATT), DNA methylation clocks (CellAgeClock, epiClockR) and BeadChip arrays (ChAMP).

Epigenomics of Common Disease

Having established the concept epigenome-wide association studies (EWAS) in 2011, we have contributed to numerous EWAS since then and are currently involed in EWAS on T1D and CKDu and related studies across several population cohorts (BIOPATH, HAPIEE, CRELES).

Epigenetics of Aging

Research into healthy aging benefits from having biomarkers for different aspects of the aging process. DNA methylation has the hallmarks to make an excellent biomaker and epigenetic clocks turned out to be the most accurate molecular readout of aging to date. We develop, evaluate and use epigenetic clocks in a variety of aging contexts to assess their use as a possible molecular crystal ball for human aging. See here for mini review.

C2c: Cancer to chronic disease

Aberrant DNA methylation is an early event in carcinogenesis. Liquid biopsies hold great promise for the early detection of tumour-specific genetic and epigenetic alterations, enabling early diagnosis and improved cancer management. The aim of the C2c project is to develop novel approaches for detection of tumour-specific DNA methylation changes in cell free DNA (cfDNA) for predictive, prognostic, and diagnostic purposes.

OMEGA: A custom genOME GenerAtor

Data science plays an essential role in Genomic Medicine, including the NHS Genomic Medicine Service. However, the development of the underlying algorithms is severely hampered by data access issues. OMEGA will overcome this limitation with open access reference genomes from PGP and GIAB that will be customised by machine learning approaches with quantifiable (epi)genetic variants and signatures of clinically relevant phenotypes and diseases for diverse ancestries.

Personal Genome Project UK

Using Open Consent, PGP-UK provides genomic, epigenomic, transcriptomic and trait data under Open Access to advance personal and medical genomics and to promote Citizen Science. In addition to the data, PGP-UK provides Open Access genome, methylome and pharmacogenetics reports. Details of the Study, Resource and Analysis Pipeline are published.


The European Standards for Precision Medicine (EU-STANDS4PM) Consortium will initiate an EU-wide mapping process to assess and evaluate strategies for data-driven in silico modelling approaches. A central goal is to develop harmonised transnational standards, recommendations and guidelines that allow a broad application of predictive in silicomethodologies in personalised medicine across Europe. Our contribution is to evaluate and implement innovative strategies for sharing data more openly and effectively.

GCGR: Glioma Cellular Genetics Resource

GCGR is generating a comprehensive collection of research tools, materials and associated data to lay the foundations for future basic and translational studies into glioma.


This NIHR Blood Transplant Research Unit project aims to identify and validate donor-specific biomarkers which are predictive of recipient outcome following haematopoietic stem cell transplantation. Such biomarkers would allow guidance of treatment strategy and improved donor selection, in a personalised medicine approach to transplantation.

Multiple MS

Using a systems medicine approach, this project aims to develop personalised treatments for multiple sclerosis based on multi-omics biomarkers. Our contributions to MultipleMS consist of integrative computational analyses and the development of a DNA methylation-based biomarker for brain atrophy.