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

[ME/CFS Science Blog]

For the hit on chromosome 17, CA10 is the only candidate and it also clearly linked to neurons and synapses.

This gene encodes a protein that belongs to the carbonic anhydrase family of zinc metalloenzymes, which catalyze the reversible hydration of carbon dioxide in various biological processes. The protein encoded by this gene is an acatalytic member of the alpha-carbonic anhydrase subgroup, and it is thought to play a role in the central nervous system, especially in brain development. Multiple transcript variants encoding the same protein have been found for this gene.

So if we focus on the close-by genes, the clearest hits seem to point to neurons/synapses.

The exceptions are OLFM4 on chromosome 13, which has a clear immune connection (linked to severity of infection).

On chromosome 6p I think the butyrophilin3 and -2 homologues (BTN3A1, BTN3A2, BTN3A3, BTN2A1 and BTN2A2) seem most likely. The genes on the left that are closer are all part of a histone gene family, which encode the proteins that package DNA into chromatin - which seems less likely. The butyrophilin group also have a clear immune function: they are a immunoglobulin gene superfamily.

 

[ME/CFS Science Blog]

For the hits on chromsome 1 and 20, there are so many potential genes on the location it's harder to guess which one might be relevant.

 

[Sasha]

PEBP1 seems like the second closest to the hit on chromosome 12, next to TAOK3, which seems very stretched out.

I misread that as 'PERP1', which would have been a great name for an ME/CFS-causing gene.

(Unfortunately, this is about my level of contribution to the discussion.)

 

[richie]

Are there any data on "permitted" co morbidities? So if depression was a permitted co-mo were depression associated genes found among those with such symptoms (which may be largely reactive).

 

[forestglip]

In the lecture, they mention that the closest gene is certainly not always the causal one but it is significantly more likely to be so than further away genes. So perhaps it would be worthwhile to highlight the closest 1-2 genes for each of the hits, as these are more likely to be relevant than others.

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

Using LocusZoom (online software for uploading and viewing GWAS data that they used in the study), I looked up all of these loci. I combined them in groups of five in the following images, in order of significance, with the image on the left being the most significant group, and the uppermost locus in each image being the most significant.




Here are those top loci, in the same order. I added annotations for genes where it's at least fairly clear which gene is closest. If the gene on GeneCards (in the first summary paragraph) has a mention of brain/neuron/synapse function, then I put it in the Brain Gene column.



The ones I edited: The 6th locus looks closer to MM22SL than POU3F2. And the 11th locus looks like it overlaps 4 different genes. So I removed those from the brain gene column.

Combined with the remaining two brain-related genes that @ME/CFS Science Blog picked, CA10 and UNC13C, I found that the following genes also matched the criteria of being apparently related to the brain and being fairly clearly nearest to a locus. I put a star next to the ones where it's really clear that there's only one nearest gene.

CA10*

UNC13C

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.

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.

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.

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.

PLCL1

Predicted to enable GABA receptor binding activity and phosphatidylinositol-4,5-bisphosphate phospholipase C activity. Predicted to be involved in several processes, including gamma-aminobutyric acid signaling pathway; negative regulation of cold-induced thermogenesis; and phosphatidylinositol-mediated signaling. Predicted to be located in cytoplasm.

CACNA1E*

Voltage-dependent calcium channels are multisubunit complexes consisting of alpha-1, alpha-2, beta, and delta subunits in a 1:1:1:1 ratio. These channels mediate the entry of calcium ions into excitable cells, and are also involved in a variety of calcium-dependent processes, including muscle contraction, hormone or neurotransmitter release, gene expression, cell motility, cell division and cell death. This gene encodes the alpha-1E subunit of the R-type calcium channels, which belong to the 'high-voltage activated' group that maybe involved in the modulation of firing patterns of neurons important for information processing. Alternatively spliced transcript variants encoding different isoforms have been described for this gene.

PCDH17*

This gene belongs to the protocadherin gene family, a subfamily of the cadherin superfamily. The encoded protein contains six extracellular cadherin domains, a transmembrane domain, and a cytoplasmic tail differing from those of the classical cadherins. The encoded protein may play a role in the establishment and function of specific cell-cell connections in the brain.

VRK2* (Assumed by me as related to the brain based on connection to schizophrenia)

This gene encodes a member of the vaccinia-related kinase (VRK) family of serine/threonine protein kinases. The encoded protein acts as an effector of signaling pathways that regulate apoptosis and tumor cell growth. Variants in this gene have been associated with schizophrenia. Alternative splicing results in multiple transcript variants that differ in their subcellular localization and biological activity.

This is just one way to interpret these charts. The 4 that ME/CFS Science Blog picked may all very well be the ME/CFS genes. I was just trying to limit to where there was somewhat less ambiguity.

Edit: Corrected loci images to show colors based on European linkage disequilibrium instead of based on all populations.

 

[Kitty]

CACNA1E*

A different gene in that family, CACNA1S, is a major cause of hypokalaemic periodic paralysis.

 

[Cornwall13]

Hi

Just a few questions please.

Going forward, are there working groups/researchers already requesting decode data? If so, do we know any details?

Are pharmaceutical companies able to use the results from the decode ME and able to use existing technology/methods and start testing with existing drugs? Or, does there need to be more work/research before this can happen?

(A personal note of thanks to everyone involved in this research)

 

[Utsikt]

Going forward, are there working groups/researchers already requesting decode data? If so, do we know any details?

These are the studies that have already had their applications for using the data approved: https://www.decodeme.org.uk/approved-studies/

Are pharmaceutical companies able to use the results from the decode ME and able to use existing technology/methods and start testing with existing drugs? Or, does there need to be more work/research before this can happen?

My complete guess is that you’d probably need to know the specific genes for that, but we might still get more general hypotheses from DecodeME data that might be testable with drugs.

My layman understanding is that pharma needs things to target, and so far we don’t really know where to aim other than probably the brain.

 

[Jonathan Edwards]

Are pharmaceutical companies able to use the results from the decode ME and able to use existing technology/methods and start testing with existing drugs?

As utsikt says, there is no obvious direct lead to using any particular drug. But there may be CNS-targeting drugs that become serious options for exploratory studies.

 

[BrokenBeaktheBrave]

Are pharmaceutical companies able to use the results from the decode ME and able to use existing technology/methods and start testing with existing drugs? Or, does there need to be more work/research before this can happen?

Various excerpts from a German article today (although quotes below are behind a paywall):

"Health Minister Nina Warken (CDU) and Research Minister Dorothee Bär (CSU) spoke a few weeks ago of ‘joint impetus’ and emphasised the importance of the issue. Research into long Covid is therefore desired by the government, but funding for drug development is still lacking. ‘The industry should pay for that,’ was the response from the ministry to Scheibenbogen's application for funds to test a drug. But in practice, companies only get involved once the basic mechanisms of a disease are understood. And that is precisely why basic research is needed."

"Without government-funded basic research, hardly any pharmaceutical company will enter drug development."

"‘Before researchers in pharmaceutical companies can develop drugs to treat a disease, the disease processes must be understood at the molecular level,’ confirms Matthias Meergans from the Association of Research-Based Pharmaceutical Companies. This is the task of academic research – and it depends on public funding. ‘Only once molecular targets have been identified can pharmaceutical companies engage in larger-scale collaborations to develop therapies.’"

This post has been copied to the "DecodeME in the news" thread

 

[forestglip]

FUMA also has exactly the MAGMA cell type enrichment I was hoping for.

There are hundreds of different cell-type expression datasets to choose from to do analyses like the tissue enrichment above. I don't really know how to choose from them, or even to examine which cell-types are included before running the analysis, but these are the 5 I tested:

I decided to continue this train of thought by testing many different brain datasets. The options are split up by brain region, so I selected a few from each. Of course, I included the five datasets I tested before again so that the multiple test correction would include them. Here is the full list of 86 tested datasets:

Spoiler: Datasets tested

3_Gabitto_MTG_Human_2023_level1
3_Gabitto_MTG_Human_2023_level2
3_Gabitto_MTG_Human_2023_level3
236_Sepp2023_Cerebellum_Human_2023_group1_level1
246_Sepp2023_Cerebellum_Human_2023_group11_level1
245_Sepp2023_Cerebellum_Human_2023_group10_level1
244_Sepp2023_Cerebellum_Human_2023_group9_level1
243_Sepp2023_Cerebellum_Human_2023_group8_level1
242_Sepp2023_Cerebellum_Human_2023_group7_level1
241_Sepp2023_Cerebellum_Human_2023_group6_level1
240_Sepp2023_Cerebellum_Human_2023_group5_level1
239_Sepp2023_Cerebellum_Human_2023_group4_level1
238_Sepp2023_Cerebellum_Human_2023_group3_level1
237_Sepp2023_Cerebellum_Human_2023_group2_level1
5_Jakel_WhiteMatter_Human_2019_level1
5_Jakel_WhiteMatter_Human_2019_level2
273_Bakken2021_AdultM1_Human_2021_level1
273_Bakken2021_AdultM1_Human_2021_level2
273_Bakken2021_AdultM1_Human_2021_level3
9_Siletti_CerebralCortex.APH.MEC_Human_2022_level1
9_Siletti_CerebralCortex.APH.MEC_Human_2022_level2
10_Siletti_CerebralCortex.PaO.A43_Human_2022_level1
10_Siletti_CerebralCortex.PaO.A43_Human_2022_level2
14_Siletti_CerebralCortex.SMG_Human_2022_level1
14_Siletti_CerebralCortex.SMG_Human_2022_level2
22_Siletti_CerebralCortex.SPL.A5-A7_Human_2022_level1
22_Siletti_CerebralCortex.SPL.A5-A7_Human_2022_level2
11_Siletti_CerebralCortex.PPH.TH-TL_Human_2022_level1
11_Siletti_CerebralCortex.PPH.TH-TL_Human_2022_level2
12_Siletti_CerebralCortex.STG_Human_2022_level1
12_Siletti_CerebralCortex.STG_Human_2022_level2
13_Siletti_CerebralCortex.LIG.Idg_Human_2022_level1
13_Siletti_CerebralCortex.LIG.Idg_Human_2022_level2
15_Siletti_CerebralCortex.Ig_Human_2022_level1
15_Siletti_CerebralCortex.Ig_Human_2022_level2
16_Siletti_CerebralCortex.IFG.A44-A45_Human_2022_level1
16_Siletti_CerebralCortex.IFG.A44-A45_Human_2022_level2
17_Siletti_CerebralCortex.PoCG.S1C_Human_2022_level1
17_Siletti_CerebralCortex.PoCG.S1C_Human_2022_level2
21_Siletti_CerebralCortex.CgGC.A23_Human_2022_level1
21_Siletti_CerebralCortex.CgGC.A23_Human_2022_level2
19_Siletti_CerebralCortex.TP.A38_Human_2022_level1
19_Siletti_CerebralCortex.TP.A38_Human_2022_level2
20_Siletti_CerebralCortex.Pir_Human_2022_level1
20_Siletti_CerebralCortex.Pir_Human_2022_level2
24_Siletti_CerebralCortex.POrG.A13_Human_2022_level1
24_Siletti_CerebralCortex.POrG.A13_Human_2022_level2
26_Siletti_CerebralCortex.LiG.V1C_Human_2022_level1
26_Siletti_CerebralCortex.LiG.V1C_Human_2022_level2
320_Jorstad2023_A1_Human_2023_10x_level1
320_Jorstad2023_A1_Human_2023_10x_level2
320_Jorstad2023_A1_Human_2023_10x_level3
29_Siletti_CerebralCortex.V2_Human_2022_level1
29_Siletti_CerebralCortex.V2_Human_2022_level2
33_Siletti_CerebralCortex.MFG.A46_Human_2022_level1
33_Siletti_CerebralCortex.MFG.A46_Human_2022_level2
44_Siletti_CerebralNuclei.GP.Gpe_Human_2022_level1
44_Siletti_CerebralNuclei.GP.Gpe_Human_2022_level2
70_Siletti_Hypothalamus.HTHma.MN_Human_2022_level1
70_Siletti_Hypothalamus.HTHma.MN_Human_2022_level2
78_Siletti_Midbrain.SN_Human_2022_level1
78_Siletti_Midbrain.SN_Human_2022_level2
86_Siletti_Myelencephalon.MoAN_Human_2022_level1
86_Siletti_Myelencephalon.MoAN_Human_2022_level2
90_Siletti_Pons.PnRF_Human_2022_level1
90_Siletti_Pons.PnRF_Human_2022_level2
97_Siletti_Thalamus.PoN.LG_Human_2022_level1
97_Siletti_Thalamus.PoN.LG_Human_2022_level2
247_Zhu2023_Neocortex_Human_2023_group1_level1
247_Zhu2023_Neocortex_Human_2023_group1_level2
248_Zhu2023_Neocortex_Human_2023_group2_level1
248_Zhu2023_Neocortex_Human_2023_group2_level2
502_NM2024_Human_2024_PrefrontalCortex_level1
341_Gittings_FrontalCortex_Human_2023_Part1_level1
341_Gittings_FrontalCortex_Human_2023_Part1_level2
342_Gittings_FrontalCortex_Human_2023_Part2_level1
342_Gittings_FrontalCortex_Human_2023_Part2_level2
366_Velmeshev2023_PrePostNatal_Human_2023_group1_cortex_level1
500_NM2024_Human_2024_VagalNucleus_level1
506_Clarence_Human_2025_HippocampalFormation_PostnatalEarly_level1
510_Clarence_Human_2025_HippocampalFormation_PostnatalLate_level1
PsychENCODE_Adult
GSE104276_Human_Prefrontal_cortex_all_ages
GSE67835_Human_Cortex
GSE168408_Human_Prefrontal_Cortex_level1_Fetal
576_Xu_Human_2023_Lymph_node_ThoracicLymphNode_level1

This was a total of 1570 cell types, so the Bonferroni significance threshold is p<~.000032.

I was pleasantly surprised to find that there were cell-types significant after correction:


Basically, the GWAS genes were enriched in neurons from several different regions of the cerebral cortex.

Here is an example of the results for all cell types tested in one specific dataset (the one the most significant neuron came from, 33_Siletti_CerebralCortex.MFG.A46_Human_2022_level1):


The one that's slightly different from the rest is a specific type of neuron "Exc_L2_3_RORB_RTKN2" instead of the general cell type "neuron". The expression data for this one is from Bakken 2021 as opposed to the rest which are from Silleti 2022.

Looking at Supplementary Table 1 of the Bakken study, it says the cell type "Exc_L2_3_RORB_RTKN2" means:

Layer 2-3 Intratelencephalic human primary motor cortex Glutamatergic neuron that selectively expresses LOC101927745, and LOC105376987, and PLCH1, and RMST mRNAs

The regions these neurons correspond to are:

From Siletti 2022:
Cerebral cortex (Cx) - Middle frontal gyrus (MFG) - A46
Cerebral cortex (Cx) - Posterior intermediate orbital gyrus (POrG) - Caudal division of OFCi - A13
Cerebral cortex (Cx) - Short insular gyri - Granular insular cortex - Ig
Cerebral cortex (Cx) - Inferior frontal gyrus (IFG) - Ventrolateral prefrontal cortex - A44-A45
Cerebral cortex (Cx) - Superior Temporal Gyrus - STG
Cerebral cortex (Cx) - Lingual gyrus (LiG) - Primary Visual Cortex - V1C
Cerebral cortex (Cx) - Postcentral gyrus (PoCG) - Primary somatosensory cortex - S1C
Cerebral cortex (Cx) - Supramarginal gyrus (SMG)

From Bakken 2021:
Primary motor cortex - Adult

I'd love for someone to verify my results are sound. But it's reassuring that significant neurons are specifically from the cortex and frontal cortex, because those are the two most significant regions in the official tissue enrichment from the study. Though note that cortex datasets made up over half of the datasets I tested, so it was somewhat cortex-biased.

I'll attach the full results file with all cell types that were tested.

 

[Hoopoe]

Is what's causing PEM activity of the brain in excess of what it can tolerate due to some problem at the synapses?

Is there any indication that glutaminergic synapses are playing a role in ME/CFS?

What's the similarity between autism and ME/CFS?

 

[mariovitali]

Yes I also agree that glutamate metabolism may be something to research further

 

[hotblack]

Like @forestglip and @ME/CFS Science Blog I’ve spent some time digging into some tools including magma/fuma and annovar. And learnt a bit about the myriad hurdles and technical issues bioinformatics students and researchers must live with. After a week much of my understanding is likely way off but I have a few questions, maybe they’re best for @Chris Ponting and the authors but perhaps others with more experience can chime in?

Were the GWAS summary statistics (hg38) or other/raw data used for MAGMA/FUMA analysis? What reference panel was used? These tools and the default panels seem to depend upon different human genome versions/reference assemblies than hg38, was liftover used or re-calling? I’m unsure about some other settings like the window size too, which could effect things quite a lot.

Knowing more about the methods, data and settings here would help reproducibility and understanding I think. And I wonder about what risks/loss there are with conversions between formats?

This study used an additive model I think, would it be worth exploring results from recessive/dominant models too?

I have learned new words like exonic, intronic and intergenic and as far as I can tell most findings (88%) from GWAS studies are in these interonic and intergenic regions. Although these don’t directly change the protein, it seems they can modify things a lot by effecting expression through things like translation.

There seems lots of scope for more analysis here, understanding what is in LD with the identified SNPs and using more tools like annotation to help get an idea of potential mechanisms. Or did the eQTL and risk loci cover this? I’ve not started looking at that yet and don’t understand the pros/cons of different methods.

Is this sort of wider analysis planned? Is the hope others may take it up? Would it be possible to make more of the data available to help others do this?

A bit of a mishmash of questions and thoughts….

 

[hotblack]

This lecture is interesting and relevant to our discussion:
MPG Primer: Linking SNPs with genes in GWAS (2022)

Oh thanks for this! I was just listening to this one from the same institute
MPG Primer: Genome-Wide Association Studies (GWAS): A Refreshed Perspective (2024)

 

[ME/CFS Science Blog]

Don't think we have discussed this plot yet:

Basically, the idea is that if the difference between groups is driven by selection bias, stratification effects, ancestry differences etc., then there will be lower p-values across the board. It would look like a systematic shift where many SNPs are affected, too many to be explained by genetic differences underlying a disease.

A true effect of the genetic susceptibility for a disease should result in a smaller number of SNPs that are significantly different.

One way to test and differentiate between the two is to plot the p-values found against a uniform distribution of p-values (because that's what we would expect if there was no effect). If we're testing the genetics of the disease, then we would see the observed p-values differ from the expected, only at the very end, like a tail that bends upward. If there were a systematic difference, then the observed and expected would not match for many other p-values, and so the straight line would break and bend earlier. It would not match a straight line through the origin with a slope of 1.

I have little experience with interpreting these plots, but it looks like there was some systematic difference for the low allele frequencies and no or much less for the common frequencies, which is where most of the hits were found. Because the observed p-values go all the way up to 10^-11, I assume this shows the full data before filtering based on quality control took place? EDIT: see comment from forestglip below.

There is also an inflation measure, lambda = 1.066, which is the ratio of the observed to expected median p-value. Here, the same reasoning applies: these should be similar (so lambda should be close to 1). If not, it would suggest population differences other than having ME/CFS or not. A lambda of 1.066 looks fine.

 

[ME/CFS Science Blog]

A lambda of 1.066 looks fine.

I also read that the intercept of an LD score regression can be interpreted in a similar way (an indicator of inflation). But I don't see this measure reported in the DecodeME preprint: anyone saw it somewhere?

 

[forestglip]

Because the observed p-values go all the way up to 10^-11, I assume this shows the full data before filtering based on quality control took place?

Why do you say that? The top hits on chr20 were around that p-value.

 

[Chris Ponting]

Like @forestglip and @ME/CFS Science Blog I’ve spent some time digging into some tools including magma/fuma and annovar. And learnt a bit about the myriad hurdles and technical issues bioinformatics students and researchers must live with. After a week much of my understanding is likely way off but I have a few questions, maybe they’re best for @Chris Ponting and the authors but perhaps others with more experience can chime in?

Were the GWAS summary statistics (hg38) or other/raw data used for MAGMA/FUMA analysis? What reference panel was used? These tools and the default panels seem to depend upon different human genome versions/reference assemblies than hg38, was liftover used or re-calling? I’m unsure about some other settings like the window size too, which could effect things quite a lot.

Knowing more about the methods, data and settings here would help reproducibility and understanding I think. And I wonder about what risks/loss there are with conversions between formats?

This study used an additive model I think, would it be worth exploring results from recessive/dominant models too?

I have learned new words like exonic, intronic and intergenic and as far as I can tell most findings (88%) from GWAS studies are in these interonic and intergenic regions. Although these don’t directly change the protein, it seems they can modify things a lot by effecting expression through things like translation.

There seems lots of scope for more analysis here, understanding what is in LD with the identified SNPs and using more tools like annotation to help get an idea of potential mechanisms. Or did the eQTL and risk loci cover this? I’ve not started looking at that yet and don’t understand the pros/cons of different methods.

Is this sort of wider analysis planned? Is the hope others may take it up? Would it be possible to make more of the data available to help others do this?

A bit of a mishmash of questions and thoughts….

Hi. Yes, we needed to LiftOver DNA variants from human genome build 38 (hg38) to the previous version (GRCh37 (hg19)) which FUMA uses for, e.g., MAGMA analysis. You're correct that some SNPs are lost in the LiftOver, but only a few. More variants are lost because FUMA's reference panel of variants is incomplete. Yes, the GWAS model we used in REGENIE is an additive linear model. But we are also applying non-additive, non-linear models separately (TarGene; will report when we have results). Yes - as with nearly all GWAS - are lead SNPs are not in protein-coding sequence. And, yes, there is scope for a lot more analyses both by us (DecodeME) and any researchers who receive the consented and pseudoanonymised data via the Data Access Committee. The preprint's title starts with "Initial Results..." and we mean this. Yet more to come.

 

[Hoopoe]

@Chris Ponting thank you for your work and dedication.