Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

[forestglip]

Some posts about a previously planned GWAS in the USA were moved to:

USA: Center for Solutions for ME/CFS - news and updates from Columbia University's NIH funded center, Lipkin

 

[ME/CFS Science Blog]

Coming back to OLMF4 and NEGR1, two genes which had significant SNPs close to them in a big depression GWAS:
Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression | Nature Genetics

DecodeME also has SNPs close to these genes that were close to reaching the significant threshold of 5*10^-8. The blue dotted line indicates the lead SNP found in the depression GWAS. The DecodeME data has a similar signal, but it's slightly different (as the coloc analysis of DecodeME already showed for OLFM4).

 

[ME/CFS Science Blog]

Also checked for significant hits in Long Covid such as the FOXP4 gene highlighted by Lammi et al. 2025 (rs9367106)
Genome-wide association study of long COVID | Nature Genetics

And the 3 SNPs highlighted by Chaudhary et al. but DecodeME didn't have a signal anywhere near these regions.


Multi-ancestry GWAS of Long COVID identifies immune-related loci and etiological links to chronic fatigue syndrome, fibromyalgia and depression | medRxiv

 

[ME/CFS Science Blog]

The coloc analysis doesn't seem that difficult (there's an R package for it, suspect it's mainly about getting the two GWAS summary data in the right format). But unfortunately, it looks like you have to request access to get the GWAS data on depression.
PGC major depressive disorder GWAS | Knowledge Portal Network

There's a data acces portal on the website of the Psychiatric Genomics Consortium
MDD Working Group – PGC

 

[forestglip]

But unfortunately, it looks like you have to request access to get the GWAS data on depression.

It looks like you can download summary stats on the GWAS Atlas website.

I'm not sure if it has data from exactly the same study you linked, but it has several for depression. For example, there's a depression trait which looks like it has NEGR1 as significant. Clicking the link next to "File" gives a 625 MB file with these columns:

chr rsid pos A2 A1 AlleleFreq ImputationAccuracy Beta StandardError P

 

[ME/CFS Science Blog]

I thought it might be useful to extend this to more loci than the top 8. Supplementary table 3 has the top 25 loci.

Did something similar by looking at SNPs that had a p-value below 5*10^-7 but that didn't appear in 8 regions that DecodeME already highlighted.

So they were just below the threshold of 5*10^-8 for statistical significance. But because this threshold is a bit rough and arbitrary, it might be useful to look at the signals just below it. Here's what I got:

ID​	p-value​	Odds ratio​	Frequency​	Closest genes​
1:69696474:A:G​	2.06e-7​	1.09​	0.18​	LRRC7​
1:73126414:C:CA​	1.19e-7​	1.07​	0.49​	NEGR1, LRRIQ3​
1:91028158:C:T​	1.89e-7​	1.07​	0.44​	ZNF64, BARHL2​
6:4336259:T:C​	2.90e-7​	0.92​	0.19​	(unclear)​
11:16217844:C:G​	1.08e-7​	1.12​	0.10​	SOX6​
12:123924955:G:A​	2.43e-7​	1.07​	0.32​	CCDC92, DNAH10 (unclear)​
17:11325637:G:C​	8.25e-8​	0.93​	0.51​	SHISA6​
18:53232948:C:T​	2.48e-7​	1.07​	0.53​	DCC​

The graphs below show protein-coding genes close to the SNP with the lowest p-value in the new region. The blue dashed line shows the location of that top SNP. I started with a window of 1Mb but in some cases (Chromosomes 12 and 17) I narrowed it down if there were many genes in the region.

CHROMSOME 1






CHROMSOME 6



CHROMSOME 11


CHROMOSOME 12


CHROMSOME 17


CHROMSOME 18

 

[ME/CFS Science Blog]

Highlighting the gene cards of some of the closest genes that have little competition or that have the top SNP inside it (Forestglip already posted about many of these before).

LRRC7

Predicted to enable protein kinase binding activity. Predicted to be involved in several processes, including establishment or maintenance of epithelial cell apical/basal polarity; positive regulation of neuron projection development; and protein localization to membrane. Located in several cellular components, including centrosome; cytosol; and nucleoplasm. Implicated in cocaine dependence.

LRRC7 Gene - GeneCards | LRRC7 Protein | LRRC7 Antibody

SOX6

This gene encodes a member of the D subfamily of sex determining region y-related transcription factors that are characterized by a conserved DNA-binding domain termed the high mobility group box and by their ability to bind the minor groove of DNA. The encoded protein is a transcriptional activator that is required for normal development of the central nervous system, chondrogenesis and maintenance of cardiac and skeletal muscle cells. The encoded protein interacts with other family members to cooperatively activate gene expression. Alternative splicing results in multiple transcript variants.

SOX6 Gene - GeneCards | SOX6 Protein | SOX6 Antibody

CCDC92 (UNCLEAR)

Enables identical protein binding activity. Predicted to be involved in innate immune response and regulation of defense response to virus. Located in centriole; centrosome; and nucleoplasm.

CCDC92 Gene - GeneCards | CCD92 Protein | CCD92 Antibody

SHISA6

Predicted to enable ionotropic glutamate receptor binding activity. Predicted to be involved in several processes, including excitatory chemical synaptic transmission; modulation of chemical synaptic transmission; and negative regulation of canonical Wnt signaling pathway. Predicted to be located in asymmetric, glutamatergic, excitatory synapse. Predicted to be part of AMPA glutamate receptor complex. Predicted to be active in dendritic spine membrane; postsynaptic density; and postsynaptic membrane.

SHISA6 Gene - GeneCards | SHSA6 Protein | SHSA6 Antibody

DCC

This gene encodes a netrin 1 receptor. The transmembrane protein is a member of the immunoglobulin superfamily of cell adhesion molecules, and mediates axon guidance of neuronal growth cones towards sources of netrin 1 ligand. The cytoplasmic tail interacts with the tyrosine kinases Src and focal adhesion kinase (FAK, also known as PTK2) to mediate axon attraction. The protein partially localizes to lipid rafts, and induces apoptosis in the absence of ligand. The protein functions as a tumor suppressor, and is frequently mutated or downregulated in colorectal cancer and esophageal carcinoma.

DCC Gene - GeneCards | DCC Protein | DCC Antibody

 

[ME/CFS Science Blog]

Don't want to beat the same drum but these secondary hits also point to neuronal development, synapses, synaptic transmission etc.

 

[ME/CFS Science Blog]

DCC

As the DecodeME preprint highlights, DCC has repeatedly been associated with chronic pain including in this GWAS of Analgesic Use

GWAS of Extended Prescription Analgesic Use Identifies Novel Genetic Loci in Chronic Pain

Pain-related conditions are the leading cause of disability worldwide. Current genome-wide association studies (GWAS) for chronic pain have mainly focused on individual pain-related disorders, which may not optimally capture the phenotype. Here, we applied a novel method of defining chronic pain...

www.medrxiv.org

 

[ME/CFS Science Blog]

See quite some similarity between this GWAS on multisite chronic pain.
Genome-wide association study of multisite chronic pain in UK Biobank | PLOS Genetics

Heritability around 10%, MAGMA only points to brain regions, DCC, CA10 and SOX6 as significant hits, genetic correlation with depression of around 0.5. They conclude:

We identified 76 independent genome-wide significant SNPs associated with MCP across 39loci. The genes of interest had diverse functions, but many were implicated in nervous-system development, neural connectivity and neurogenesis

A lot of the loci are different from those in DecodeME though. The NEGRI and OLFM4 loci show more similarity to depression GWAS findings.

 

[ME/CFS Science Blog]

Coming back to @Woolie 's argument that the SNPs might reflect differences in non-ME/CFS factors such as ancestry, socio-economic status etc.

I think that big genetic differences due to ancestry are controlled for by adding the 20 principal components to the regression. But what I said about that the implicates genes pointing to something specific is likely not quite right.

Browsing the GWAS Catalog (see table below) I found that many of the closest genes to the hits have already been implicated in things like BMI, intelligence, height, smoking status, age at menarche, etc. I suspect the main reason is that these traits have been studied the most and with enormous sample sizes. So many hundreds of genes have been implicated with them.

Intelligence comes up a lot though. It could be that DecodeME selected people with a higher intelligence than those who participated in the UK biobank and that this explains the implicated genes and SNP hits below.

I'm a bit skeptical about this explanation because if there is a difference in intelligence between cases and controls it will likely be quite small compared to the difference disability. The odds ratios in DecodeME also look larger (> 1.07) than those for educational attainment or intelligence or things like smoking (usually < 1.04). So I still think it's much more likely that these SNP reflect the risk of ME/CFS rather than other factors.

Insomnia, being a morning person, depression, schizophrenia are other traits that frequently appear but probably because they are also mainly located in the brain and have been studied a lot, just like intelligence.

Location​	Implicated genes​	Also implicated in (selected examples, not the full list):​
Chromosome 1 173.50-174.70​	unclear​	/​
Chromosome 6p 26.15-26.50​	BTN3A2​	Intelligence, sexual dimorphism, BMI, schizophrenia, Multiple myeloma, Major depressive disorder​
Chromosome 6q 97.60-99.00​	POU3F2​	socioeconomic status, intelligence, smoking initiation educational attainment, mathematical ability, morning person​
Chromosome 6q 97.60-99.00​	FBXL4​	cognitive function measurement, bone density socioeconomic status, major depressive disorder, educational attainment​
Chromosome 12 118.00-118.50​	PEBP1​	Wellbeing: positive affect, few results​
Chromosome 12 118.00-118.50​	SUDS3​	prostate specific antigen amount, VSIG10 protein levels, neuroticism, loneliness, depression, Alzheimer​
Chromosome 12 118.00-118.50​	ТАОКЗ​	No results​
Chromosome 13 53.00-53.50​	OLFM4​	Insomnia, major depressive disorder, BMI, educational attainment​
Chromosome 15 54.60-55.40​	UNC13C​	Severe COVID-19 infection, memory performance, age at menarche, educational attainment, breast cancer​
Chromosome 15 54.60-55.40​	RSL24D1​	BMI, Survival in coronary artery disease, educational attainment, breast cancer​
Chromosome 17 52.05-52.40​	CA10​	Insomnia, Smoking initiation, Height, menarche age at onset, pain, educational attainment, Bacterial meningitis​
Chromosome 20 48.80-49.60​	ARFGEF2​	Intelligence, body height, BMI, subjective well-being, Alzheimer​
Chromosome 20 48.80-49.60​	CSE1L​	Intelligence, wellbeing, body fat, height, Alzheimer, loneliness​
Chromosome 20 48.80-49.60​	PREX1​	Intelligence, body height, blood pressure, colorectal cancer, multiple myeloma,​
Chromosome 20 48.80-49.60​	STAU1​	Intelligence, height, well-being, depressive symptoms, insomnia​
​	​	​
Chromosome 1 69.69​	LRRC7​	Educational attainment, bone density, BMI, adolescent idiopathic scoliosis, depression/anxiety​
Chromosome 1 73.12​	NEGR1​	Intelligence, insomnia, socioeconomic status, BMI, schizophrenia, ADHD, autism, depression​
Chromosome 1 73.12​	LRRIQ3​	Education attainment, Alzheimer,​
Chromosome 1 91.02​	ZNF64​	No results​
Chromosome 1 91.02​	BARHL2​	Smoking initiation, BMI, educational attainment, socio-economic status, body weight, morning chronotype​
Chromosome 6 43.36​	UNCLEAR​	/​
Chromosome 12 12.39​	CCDC92​	BMI, body fat, coronary artery disease​
Chromosome 12 12.39​	DNAH10​	BMI, body fat, height, insomnia, atrial fibrillation​
Chromosome 17 11.32​	SHISA6​	Myopia, sleep duration, smoking initiation, short sleep duration​
Chromosome 18 53.23​	DCC​	Intelligence, Gallbladder cancer, Insomnia, Schizophrenia, Chronic back pain, depression,​

EDIT: might an error in confusing DCC with DDC. Now corrected.

 

[forestglip]

Browsing the GWAS Catalog (see table below) I found that many of the closest genes to the hits have already been implicated in things like BMI, intelligence, height, smoking status, age at menarche, etc.

Can you give more detail for how you used to decodeme data to get these lists of traits? Search for genes? Variants? Ranges?

 

[ME/CFS Science Blog]

Can you give more detail for how you used to decodeme data to get these lists of traits? Search for genes? Variants? Ranges?

Searched for genes, like CA10. Then you get a list of other traits and GWAS where the gene has been implicated.

Selected the DecodeME genes mainly based on proximity to the SNP with the lowest p-value in the region. The bottom of the table is from regions that only reached a p value of 10^-7.

 

[forestglip]

Searched for genes, like CA10. Then you get a list of other traits and GWAS where the gene has been implicated.

Selected the DecodeME genes mainly based on proximity to the SNP with the lowest p-value in the region. The bottom of the table is from regions that only reached a p value of 10^-7.

Ok thanks. How did you select traits to highlight? For example, BTN3A2 has 97 traits, some with even lower p values than some of the traits in your table, such as height or teeth issues.

 

[ME/CFS Science Blog]

For example, BTN3A2 has 97 traits, some with even lower p values than some of the traits in your table, such as height or teeth issues.

Yeah good point, it's a bit arbitrary. Selected the ones with the most associations (those that you see if you click on 'Traits'). Those are mostly quantitative traits like height or intelligence that have been tested a lot and have big sample sizes.

So I also looked at traits with a reported odds ratio, because those are often binary traits like having an illness or not. Added those binary traits if they had a p-value lower than 5*10^-8. But I didn't try to be very comprehensive so perhaps I missed some.

EDIT: should probably have made clearer that this was only a selection of associated traits. Will update my post above.

 

[jnmaciuch]

@ME/CFS Science Blog I have a selfish question based on a random thought that just occurred to me—did your analysis with other diagnoses happen to include anything for thyroiditis? Interested in both Hashimoto’s or subacute/viral

 

[ME/CFS Science Blog]

@ME/CFS Science Blog I have a selfish question based on a random thought that just occurred to me—did your analysis with other diagnoses happen to include anything for thyroiditis? Interested in both Hashimoto’s or subacute/viral

Not that I can remember but it's quite possible that I overlooked.

 

[jnmaciuch]

Not that I can remember but it's quite possible that I overlooked.

It might not have come up anyways when cross referencing with DecodeME since a lot of risk genes for thyroiditis are on the X chromosome or in MHC. Just wanted to quickly check on the off-chance something had come up already. Thanks for responding!

 

[EndME]

It might not have come up anyways when cross referencing with DecodeME since a lot of risk genes for thyroiditis are on the X chromosome or in MHC. Just wanted to quickly check on the off-chance something had come up already. Thanks for responding!

I don't think we have any information on which conditions were exactly excluded from DecodeME (but could have included people with thyroid autoimmune conditons). Data on comorbid conditions is still forthcoming I believe and the questionnaire included questions on thyroid conditons.

 

[Jonathan Edwards]

I don't think we have any information on which conditions were exactly excluded from DecodeME (but could have included people with thyroid autoimmune conditons).

I doubt that would have skewed the result significantly. None of the known genes for Hashimoto's (e.g. DR, PTPN22) gives a high risk so most people with the risk alleles will still have got into the DecodeME patient cohort.

I amnot aware of X chromosome genes being involved. My guess is that it is the absence of Y that matters for the sex ratio.