Zhang, Sai, Jahanbani, Fereshteh, Chander, Varuna et al. · medRxiv : the preprint server for health sciences · 2025 · DOI
Researchers used advanced artificial intelligence to analyze the genes of ME/CFS patients and discovered 115 genes that may contribute to the disease. They found that people with ME/CFS have lower levels of these risk genes active in their immune cells and nervous system. This genetic analysis could eventually help doctors diagnose ME/CFS more accurately and identify new treatment targets.
This research provides the first comprehensive genetic map of ME/CFS using cutting-edge AI analysis, potentially enabling earlier diagnosis and revealing new biological pathways for treatment development. Understanding which genes and immune cells are disrupted in ME/CFS could accelerate the search for effective therapies for a disease that currently has no cure.
This study does not prove that these genetic variants directly cause ME/CFS, only that they are statistically associated with the disease. The findings are correlational rather than causal, and the study does not demonstrate that the identified genes would be effective drug targets or that correcting their expression would improve patient outcomes. External validation of the HEAL2 model's diagnostic accuracy in independent patient populations is still needed.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
The first block is for the primary paper and is the citation you should use in research work. The atlas-snapshot line only applies if you are specifically referring to this atlas’s reading of the paper on the date shown.
Primary citation
Zhang, Sai, Jahanbani, Fereshteh, Chander, Varuna, Kjellberg, Martin, Liu, Menghui, Glass, Katherine A, et al. (2025). Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis.. medRxiv : the preprint server for health sciences. https://doi.org/10.1101/2025.04.15.25325899
BibTeX
@article{mecfsatlas-zhang-2025-dissecting-genetic,
author = {Zhang, Sai and Jahanbani, Fereshteh and Chander, Varuna and Kjellberg, Martin and Liu, Menghui and Glass, Katherine A and Iu, David S and Ahmed, Faraz and Li, Han and Maynard, Rajan Douglas and Chou, Tristan and Cooper-Knock, Johnathan and Zhang, Martin Jinye and Thota, Durga and Zeineh, Michael and Grenier, Jennifer K and Grimson, Andrew and Hanson, Maureen R and Snyder, Michael P},
title = {Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis.},
journal = {medRxiv : the preprint server for health sciences},
year = {2025},
doi = {10.1101/2025.04.15.25325899},
note = {PubMed: 40321247},
url = {https://www.mecfsatlas.com/evidence/zhang-2025-dissecting-genetic},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-30. https://www.mecfsatlas.com/evidence/zhang-2025-dissecting-genetic
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