Lee, Eunjee, Cho, Seoae, Kim, Kyunga et al. · Genomics · 2009 · DOI
Researchers developed a new method to find which genes might cause ME/CFS by combining three types of medical data: genetic variations people are born with, how active those genes are in the body, and whether someone has the disease. When they tested this method on ME/CFS patients, they found that certain genes—especially one called NR3C1—appear to influence disease risk, and that patients with ME/CFS plus depression may have different genetic causes than those with ME/CFS alone.
This study addresses a fundamental challenge in ME/CFS research: identifying which genes actually cause the disease rather than merely correlating with it. By integrating genetic, molecular, and clinical data, the approach provides a framework for understanding disease heterogeneity and may explain why some ME/CFS patients have different symptom profiles or comorbidities, potentially leading to more targeted therapeutic strategies.
This study does not prove that NR3C1 or other identified genes are sufficient to cause ME/CFS, nor does it establish whether the observed associations are causally relevant in patients or merely statistical correlations. The findings are based on integrated data analysis and require functional validation and replication in independent cohorts to confirm causal mechanisms. Additionally, the cross-sectional nature of the analysis cannot establish temporal relationships between genetic changes and disease development.
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
Lee, Eunjee, Cho, Seoae, Kim, Kyunga, & Park, Taesung (2009). An integrated approach to infer causal associations among gene expression, genotype variation, and disease.. Genomics. https://doi.org/10.1016/j.ygeno.2009.06.002
BibTeX
@article{mecfsatlas-lee-2009-integrated-approach,
author = {Lee, Eunjee and Cho, Seoae and Kim, Kyunga and Park, Taesung},
title = {An integrated approach to infer causal associations among gene expression, genotype variation, and disease.},
journal = {Genomics},
year = {2009},
doi = {10.1016/j.ygeno.2009.06.002},
note = {PubMed: 19540336},
url = {https://www.mecfsatlas.com/evidence/lee-2009-integrated-approach},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-28. https://www.mecfsatlas.com/evidence/lee-2009-integrated-approach
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