Genomics and Epigenetics
1 DecodeME
Principal Investigator:: Chris Ponting, Professor of Medical Bioinformatics
Institution:: University of Edinburgh, United Kingdom
Contact/URL:: https://www.decodeme.org.uk/
Registry ID:: ISRCTN15960771
Funder:: MRC/NIHR (UK Research and Innovation)
Status:: Active (genotyping and analysis phase)
Phase:: Observational, genome-wide association study (GWAS)
Cohort:: 20,000 ME/CFS patients (DNA samples); UK-based, self-reported diagnosis with GP confirmation
Mechanism/Focus:: First large-scale GWAS for ME/CFS. Aims to identify genetic variants associated with susceptibility, severity, and symptom subtypes. DNA collected via saliva kits mailed to participants.
Primary Outcomes:: Genome-wide significant loci for ME/CFS; genetic correlation with related conditions; polygenic risk scores
Estimated Completion:: First results expected 2026
Timeline:: Recruitment launched 2022; sample collection completed 2024; genotyping in progress; first GWAS results anticipated 2026
Publication Medium:: Expected in genetics/genomics journal (e.g., Nature Genetics); open access
Document Relevance:: Ch. 12 (genetic and epigenetic factors), Ch. 20 (biomarker research), Ch. 23 (epidemiology)
DecodeME is unprecedented in ME/CFS genetics: with over 20,000 participants, it has the statistical power to detect common variants of modest effect size that previous underpowered GWAS could not identify. Genetic findings could reveal causal pathways (e.g., immune regulation, ion channel function, autonomic control) and enable Mendelian randomisation analyses to test whether associations observed in smaller studies reflect causal relationships. The study’s scale also allows subgroup analyses by onset trigger, severity, and symptom profile.
2 EpiSwitch CFS Epigenetic Diagnostic
Principal Investigator:: Ewan Hunter (lead author, validation study)
Institution:: Oxford BioDynamics, United Kingdom
Contact/URL:: https://doi.org/10.1186/s12967-025-07203-w
Registry ID:: N/A (proof-of-concept; clinical validation pending)
Funder:: Oxford BioDynamics (industry)
Status:: Proof-of-concept published October 2025; clinical validation studies planned
Phase:: Diagnostic development (pre-clinical validation)
Cohort:: Initial study: 47 severe ME/CFS patients + 61 healthy controls. Larger validation cohorts needed.
Mechanism/Focus:: EpiSwitch 3D genomic profiling of chromosome conformation captures (CCs) in blood. Identified a 200-marker epigenetic model. Pathways implicated: interleukins, TNF\(\alpha\), neuroinflammation, toll-like receptor signalling, JAK/STAT.
Primary Outcomes:: Sensitivity 92%, specificity 98%, overall accuracy 96% in proof-of-concept validation cohort
Estimated Completion:: Clinical validation timeline not yet announced
Timeline:: Proof-of-concept published October 2025; independent replication and prospective validation required before clinical use
Publication Medium:: Journal of Translational Medicine (published); future validation studies TBD
Document Relevance:: Ch. 12 (genetic and epigenetic factors), Ch. 20 (biomarker research), Ch. 25 (translational findings)
The EpiSwitch proof-of-concept achieved striking diagnostic accuracy (96%), but the small sample size (\(n=108\)) and retrospective design warrant caution. If validated in larger, prospective, multi-site cohorts with disease controls, this approach could provide the first epigenetic blood test for ME/CFS. The implicated pathways (JAK/STAT, TLR signalling, neuroinflammation) align with immune findings reported in Chapters 7 and 8, providing convergent evidence from an independent methodological angle. The critical next step is independent replication with blinded samples.
3 PrecisionLife Combinatorial Genomics
Principal Investigator:: Steve Gardner (CEO, PrecisionLife)
Institution:: PrecisionLife Ltd, Oxford, United Kingdom
Contact/URL:: https://precisionlife.com/news-and-events/me-genetics-study
Registry ID:: N/A (secondary analysis of DecodeME and UK Biobank data)
Funder:: PrecisionLife (industry) with access to DecodeME consortium data
Status:: Published December 2025
Phase:: Computational genomics (AI-led combinatorial analytics)
Cohort:: Two DecodeME cohorts + UK Biobank; results confirmed across three independent datasets
Mechanism/Focus:: Applied combinatorial analytics platform to identify gene–gene interaction networks in ME/CFS. Unlike standard GWAS (which tests single variants), this approach identifies combinations of variants that jointly confer risk.
Primary Outcomes:: Identified 250+ core genes associated with ME/CFS, of which 76 genes overlap with long COVID. Networks cluster around immune regulation, metabolic function, and neurological pathways.
Estimated Completion:: Published
Publication Medium:: PrecisionLife press release and preprint; peer-reviewed publication expected
Document Relevance:: Ch. 12 (genetic factors), Ch. 13 (integrative models), Ch. 14 Speculative Cross-Disease Connections (long COVID overlap)
The PrecisionLife analysis complements the DecodeME GWAS (Section Genomics and Epigenetics) by detecting higher-order gene–gene interactions that conventional single-variant approaches miss. The 76-gene overlap with long COVID provides the strongest genetic evidence yet for shared biological mechanisms between the two conditions, supporting the hypothesis explored in Ch. 14 Speculative Cross-Disease Connections. However, combinatorial approaches can generate false positives when the number of tested combinations is large; independent replication (ideally in non-European ancestries) is essential before clinical or mechanistic conclusions are drawn. (PrecisionLife 2025)