Summary
A medRxiv preprint reports seven genomic risk loci associated with myalgic encephalomyelitis/chronic fatigue syndrome after analysis of UK Biobank and All of Us data. The findings provide leads for studying the condition’s biology, but the study is based on a preprint and no locus was significant across all three cohorts.
Researchers have identified seven genomic regions associated with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) in analyses of the UK Biobank and the US All of Us Research Program. The findings are reported in a medRxiv preprint posted on September 14, 2026, and provide genetic leads for investigating a condition whose biological basis remains poorly understood.
The study began with 1,268 ME/CFS cases in the UK Biobank. The researchers used electronic health records and survey responses to classify cases and non-ME/CFS controls, then applied the TarGene method to conduct a genome-wide association study. This type of analysis tests whether differences in DNA sequence occur more often among people with a particular condition than among controls.
The initial analysis found 176 variants significantly associated with ME/CFS at a false discovery rate below 5%. A false discovery rate is a statistical measure used to limit the expected proportion of findings among results labelled significant.
Replication across biobank cohorts
To test whether the initial signals were reproducible, the researchers conducted two further analyses in separate cohorts: another UK Biobank group containing 319 cases and a cohort from the All of Us Research Program containing 371 cases. These analyses used similar approaches to identify ME/CFS status.
Seven genomic risk loci replicated across the discovery and replication work. A locus is a region of the genome that may contain one or more variants associated with a trait. The replicated regions included genes such as BICD1, GRIN2A, CSMD1 and RORA.
The replication result has an important qualification: none of the seven loci reached significance across all three cohorts. The study therefore presents them as reproducible genetic signals across parts of the analysis rather than as uniformly confirmed findings in every cohort.
A closer look at one genetic signal
The researchers used fine-mapping at one replicated locus to narrow the association to a credible set of variants. That set colocalised with reduced expression of CLYBL in the putamen, a brain region, and was in linkage disequilibrium with the replicated variant. Linkage disequilibrium describes the tendency of nearby genetic variants to be inherited together, meaning that a detected variant can act as a marker for another variant contributing to the statistical signal.
The specific CLYBL Arg259 stop-gain variant, however, was not associated with ME/CFS risk in the analysis. This distinction matters because the result points to a genomic region and an expression pattern without identifying that particular variant as the responsible change.
The study also tested whether genetic associations varied by sex or by deprivation. Neither gene-by-sex nor gene-by-deprivation interaction remained significant after correction for multiple testing.
What the findings add
ME/CFS is described by the authors as a debilitating, female-biased disease with no diagnostic biomarker, effective treatment or well-understood cause. Genetic association studies can help identify biological pathways for further research and can indicate which genes or tissues deserve closer investigation.
The results are research findings rather than a diagnostic test or a treatment. A genomic association is a statistical relationship between genetic variation and disease status; establishing how a signal affects biology requires additional functional and clinical research. The CLYBL result offers a possible connection between a replicated locus and gene expression in the putamen, while the other loci provide additional candidates for investigation.
Because this report is a medRxiv preprint, its findings are presented before formal peer review. The analysis used existing biobank data, with ME/CFS classification based on health records and survey responses. The authors report that the datasets and analysis code are available through the UK Biobank and All of Us access systems and linked repositories, supporting further examination of the reported associations.