GWAS/WGS Meta-Analysis and ME/CFS Genetic Studies

1 Maccallini 2026 — Biological Insights from GWAS and WGS of ME/CFS

(Maccallini 2026)

Key Findings::

- Meta-GWAS and post-GWAS analysis of 19,470 ME/CFS cases
- Brain tissue enrichment, glutamatergic synapse involvement, specific neuronal cell types
- Integrates DecodeME, MVP, and UK Biobank summary statistics
- Post-GWAS analyses (LDSC, MAGMA, FUMA) point to CNS-specific mechanisms

Conclusion:: Largest ME/CFS meta-GWAS to date; provides converging evidence for brain-based genetic architecture. Limitations:: Preprint; not yet peer-reviewed; methodology sound but unvalidated. Certainty:: 0.45

2 DecodeME Consortium 2025 — Initial GWAS Findings

(DecodeME Consortium, Ponting, et al. 2025)

Key Findings::

- 8 genome-wide significant loci in up to 15,579 cases, 259,909 controls
- Three loci near infection-response genes (BTN2A2, OLFM4, RABGAP1L)
- Four of eight replicated in UK Biobank/Lifelines
- CA10 colocalises with multisite chronic pain
- No shared causal variants with depression or anxiety

Conclusion:: Both immunological and neurological processes involved in ME/CFS genetic risk. Limitations:: Preprint; European ancestry only; fine-mapping not conclusive for causal genes. Certainty:: 0.60

3 Sardell et al. 2026 — Combinatorial Genetic Risk Factors in DecodeME

(Sardell et al. 2026)

Key Findings::

- Reproducible combinatorial genetic risk factors across DecodeME and independent cohorts
- Synergistic SNP interactions not detectable by standard GWAS
- Shared genetic architecture across ME/CFS and comorbidities

Conclusion:: Combinatorial analytics reveal additional genetic risk architecture invisible to conventional GWAS. Limitations:: Proprietary PrecisionLife platform; independent replication needed. Certainty:: 0.55

4 Birch et al. 2025 — Rare Monogenic Variation in ME/CFS

(Birch et al. 2025)

Key Findings::

- Rare monogenic variation contributes to ME/CFS genetic architecture
- Complements common variant GWAS findings
- Identifies rare variant burden in neurological and immune pathways

Conclusion:: Precision genomics approach reveals rare variation contribution. Limitations:: Limited sample size for rare variant analysis; single study. Certainty:: 0.50

5 Huang et al. 2026 — Metabolite GWAS in ME/CFS

(Huang et al. 2026)

Key Findings::

- GWAS on plasma biomarker levels in ME/CFS patients (UK Biobank)
- Genetic susceptibility toward a specific metabolic phenotype
- Identifies variants linking metabolic perturbations to ME/CFS risk

Conclusion:: Genetic basis for metabolic phenotype in ME/CFS. Limitations:: UK Biobank case definition; metabolic proxy not direct ME/CFS measure. Certainty:: 0.55

6 Steen et al. 2026 — Shared Genetic Risk Across Functional Somatic Syndromes

(Steen et al. 2026)

Key Findings::

- Twin-sibling study estimating genetic/environmental contributions
- Shared heritability between CFS, fibromyalgia, IBS, internalizing disorders
- Genetic overlap with immune-mediated diseases

Conclusion:: Substantial shared genetic architecture across functional somatic syndromes. Limitations:: Self-reported diagnoses; twin-sibling design cannot fully separate shared environment. Certainty:: 0.65

7 Hirsch et al. 2025 — Comparative GWAS (PTLDS, FM, ME/CFS)

(Hirsch et al. 2025)

Key Findings::

- Compared genetic architecture across post-Lyme, FM, and ME/CFS
- Identifies shared and distinct genetic factors

Conclusion:: Cross-condition GWAS reveals condition-specific and shared loci. Limitations:: Small sample sizes for PTLDS subgroup. Certainty:: 0.50

8 Duan et al. 2025 — Mendelian Randomization: Immune Cells → ME/CFS

(Duan et al. 2025)

Key Findings::

- MR supports causal role for specific immune cell types in ME/CFS
- Inflammatory cytokines identified as mediators

Conclusion:: Genetically predicted immune profiles causally influence ME/CFS risk. Limitations:: MR assumptions; pleiotropy; modest effect sizes. Certainty:: 0.50

9 Wirth & Scheibenbogen 2026 — Glutamatergic/GABAergic Imbalance in ME/CFS

(Wirth and Scheibenbogen 2026)

Key Findings::

- ME/CFS and post-COVID share excitatory/inhibitory neurotransmitter imbalance
- "Wired but tired" explained by glutamatergic excess / GABAergic deficit
- Integrates genetic findings pointing to glutamatergic synapse

Conclusion:: Neurotransmitter imbalance provides mechanistic link between genetic findings and symptoms. Limitations:: Review paper; synthesizes existing evidence rather than new data. Certainty:: 0.55

10 Hajdarevic et al. 2022 — Genetic Association Study (n=2,532)

(Hajdarevic et al. 2022)

Key Findings::

- Largest published GWAS at the time (2,532 cases)
- Several potential risk loci identified
- No genome-wide significant loci

Conclusion:: Underpowered for GWAS; highlights need for larger samples. Limitations:: Below genome-wide significance threshold. Certainty:: 0.55

11 Schlauch et al. 2016 — First ME/CFS GWAS

(Schlauch et al. 2016)

Key Findings::

- 442 SNPs identified as candidate associations
- First published ME/CFS GWAS
- No genome-wide significant loci

Conclusion:: Pioneering study; establishes feasibility of GWAS in ME/CFS. Limitations:: Severely underpowered by modern standards. Certainty:: 0.40

12 Ueland et al. 2022 — Failed Replication of TRA Locus

(Ueland et al. 2022)

Key Findings::

- Failed to replicate previously reported TRA locus association
- Important negative control for the field

Conclusion:: TRA locus finding likely false positive; field needs rigorous replication standards. Limitations:: Single replication study; different population. Certainty:: 0.60

13 Dibble et al. 2020 — Critical Review of ME/CFS Genetics

(Dibble, McGrath, and Ponting 2020)

Key Findings::

- Comprehensive review of heritability and genetic risk factors
- Documents small sample sizes and lack of replication
- Calls for adequately powered GWAS (subsequently addressed by DecodeME)

Conclusion:: Heritable component established; specific loci elusive pre-DecodeME. Limitations:: Review paper; no new data. Certainty:: 0.70

14 Song et al. 2025 — MDD-ME/CFS Mendelian Randomization

(Song et al. 2025)

Key Findings::

- Two-sample MR investigating MDD-ME/CFS causal relationship
- Complements DecodeME finding of no shared causal variants

Conclusion:: Genetic evidence does not support causal MDD→ME/CFS pathway. Limitations:: MR assumptions; GWAS summary statistic limitations. Certainty:: 0.50

15 Das et al. 2022 — Combinatorial Analysis (UK Biobank)

(Das et al. 2022)

Key Findings::

- GWAS + PrecisionLife combinatorial analytics on UK Biobank
- Identified genetic risk factors missed by conventional GWAS
- Synergistic SNP-SNP interactions

Conclusion:: Combinatorial approaches complement standard GWAS. Limitations:: Proprietary platform; replication needed. Certainty:: 0.55

16 Frank et al. 2026 — Molecular Reclassification of ME/CFS

(Frank et al. 2026)

Key Findings::

- Integrates multi-omics, machine learning, and precision medicine
- Toward molecular reclassification of ME/CFS subtypes

Conclusion:: Multi-omics integration can redefine ME/CFS classification. Limitations:: Review/hypothesis paper; requires empirical validation. Certainty:: 0.45

References

Birch, C. L., B. M. Wilk, M. Gajapathy, A. Brown, T. Cox, P. A. Wilmarth, D. W. Fardo, and S. Estus. 2025. “Uncovering the Genetic Architecture of ME/CFS: A Precision Approach Reveals Impact of Rare Monogenic Variation.” Journal of Translational Medicine 23: 1345. https://doi.org/10.1186/s12967-025-07586-w.

Das, S., K. Taylor, J. Kozubek, J. Sardell, and A. Gardner. 2022. “Genetic Risk Factors for ME/CFS Identified Using Combinatorial Analysis.” Journal of Translational Medicine 20: 598. https://doi.org/10.1186/s12967-022-03815-8.

DecodeME Consortium, Chris P Ponting, et al. 2025. “Initial Findings from the DecodeME Genome-Wide Association Study of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” medRxiv. https://doi.org/10.1101/2025.08.06.25333109v1.

Dibble, Joshua J, Simon J McGrath, and Chris P Ponting. 2020. “Genetic Risk Factors of ME/CFS: A Critical Review.” Human Molecular Genetics 29 (R1): R117–24. https://doi.org/10.1093/hmg/ddaa169.

Duan, L., J. Yang, J. Zhao, P. Wang, and R. Li. 2025. “Evaluating the Causal Role of Genetically Inferred Immune Cells and Inflammatory Cytokines on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Biomedicines 13 (5): 1200. https://doi.org/10.3390/biomedicines13051200.

Frank, J., N. Nesterovitch, C. Movva, P. E. Lipsky, Z. Illes, and D. L. Peterson. 2026. “Toward a Molecular Reclassification of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Integrating Multi-Omics, Machine Learning, and Precision Medicine.” International Journal of Molecular Sciences 27 (10): 4436. https://doi.org/10.3390/ijms27104436.

Hajdarevic, R., A. Lande, J. Mehlsen, A. Rydland, S. Sæbø, E. Davidsen, M. K. Viken, Ø. Fluge, O. Mella, and B. A. Lie. 2022. “Genetic Association Study in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) Identifies Several Potential Risk Loci.” Brain, Behavior, and Immunity 102: 362–69. https://doi.org/10.1016/j.bbi.2022.03.010.

Hirsch, A. G., A. E. Justice, A. Poissant, J. N. Williams, J. L. Weissfeld, K. Alderink, T. Gordon, et al. 2025. “A Comparison of Genome-Wide Association Analyses of Persistent Symptoms After Lyme Disease, Fibromyalgia, and Myalgic Encephalomyelitis - Chronic Fatigue Syndrome.” BMC Infectious Diseases 25: 194. https://doi.org/10.1186/s12879-024-10238-x.

Huang, K., M. Muneeb, N. Thomas, J. Huang, and L. Wang. 2026. “Exploring a Genetic Basis for the Metabolic Perturbations in ME/CFS Using UK Biobank.” iScience 29: 114316. https://doi.org/10.1016/j.isci.2025.114316.

Maccallini, P. 2026. “Biological Insights from Genome-Wide Association Studies and Whole Genome Sequencing of Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome.” Research Square [Preprint], June. https://doi.org/10.21203/rs.3.rs-9702020/v1.

Sardell, Jessica M., S. Das, M. Pearson, J. Kozubek, K. Taylor, and A. Gardner. 2026. “Identification of Novel Reproducible Combinatorial Genetic Risk Factors for Myalgic Encephalomyelitis in the DecodeME Patient Cohort and Commonalities.” Journal of Translational Medicine 24: 420. https://doi.org/10.1186/s12967-026-08167-1.

Schlauch, K. A., S. F. Khaiboullina, K. L. De Meirleir, S. Rawat, J. Petereit, A. A. Rizvanov, N. Blatt, et al. 2016. “Genome-Wide Association Analysis Identifies Genetic Variations in Subjects with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Translational Psychiatry 6: e730. https://doi.org/10.1038/tp.2015.208.

Song, W., X. Hou, M. Wu, Z. Li, Y. Zhang, and J. Wang. 2025. “Relationship Between Major Depressive Disorder and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Two-Sample Mendelian Randomization Study.” Scientific Reports 15: 1350. https://doi.org/10.1038/s41598-025-85217-6.

Steen, O. D., H. Ohlsson, S. L. van Ockenburg, K. S. Kendler, J. Sundquist, and K. Sundquist. 2026. “Shared Genetic Risk Between Functional Somatic Syndromes, Internalizing Disorders, and Immune-Mediated Diseases: A Twin-Sibling Study.” Brain, Behavior, and Immunity 117: 1–10. https://doi.org/10.1016/j.bbi.2026.106837.

Ueland, M., R. Hajdarevic, O. Mella, Ø. Fluge, B. A. Lie, and M. K. Viken. 2022. “No Replication of Previously Reported Association with Genetic Variants in the T Cell Receptor Alpha (TRA) Locus for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Translational Psychiatry 12: 277. https://doi.org/10.1038/s41398-022-02046-1.

Wirth, Klaus J., and Carmen Scheibenbogen. 2026. “Imbalance of Excitatory and Inhibitory Neurotransmitter Systems in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” International Journal of Molecular Sciences 27 (9): 4041. https://doi.org/10.3390/ijms27094041.