Das, Sayoni, Taylor, Krystyna, Kozubek, James et al. · Journal of translational medicine · 2022 · DOI
Researchers identified 199 genetic variations in 14 genes that are associated with ME/CFS by analyzing genetic data from thousands of people in UK Biobank. These genetic variations work together in groups to influence ME/CFS risk and were found in 91% of people with ME/CFS in the study. The identified genes are involved in processes the body uses to handle stress, infection, energy production in cells, sleep, and immune function—all areas that scientists think may go wrong in ME/CFS.
This is the first large-scale genetic study to identify specific gene combinations associated with ME/CFS and connect them to proposed biological mechanisms of the disease. These findings may eventually enable better diagnostic strategies and identify new treatment targets for patients currently lacking effective therapeutic options.
This study identifies genetic associations but does not prove these genetic variations cause ME/CFS—correlation does not equal causation. The study also does not explain how these genetic variations lead to disease symptoms or whether they predict who will develop ME/CFS versus who already has it. Finally, limited replication across other fatigue conditions suggests these findings may be specific to ME/CFS rather than general fatigue disorders.
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
Das, Sayoni, Taylor, Krystyna, Kozubek, James, Sardell, Jason, & Gardner, Steve (2022). Genetic risk factors for ME/CFS identified using combinatorial analysis.. Journal of translational medicine. https://doi.org/10.1186/s12967-022-03815-8
BibTeX
@article{mecfsatlas-das-2022-genetic-risk,
author = {Das, Sayoni and Taylor, Krystyna and Kozubek, James and Sardell, Jason and Gardner, Steve},
title = {Genetic risk factors for ME/CFS identified using combinatorial analysis.},
journal = {Journal of translational medicine},
year = {2022},
doi = {10.1186/s12967-022-03815-8},
note = {PubMed: 36517845},
url = {https://www.mecfsatlas.com/evidence/das-2022-genetic-risk},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-29. https://www.mecfsatlas.com/evidence/das-2022-genetic-risk
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