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

forestglip said:
Blockade of OX40/OX40L signaling using anti-OX40L alleviates murine lupus nephritis (2024)
Genetic variants of the OX40 ligand (OX40L) locus are associated with the risk of systemic lupus erythematosus (SLE), it is unclear how the OX40L blockade delays the lupus phenotype. Therefore, we examined the effects of an anti-OX40L antibody in MRL/Lpr mice. Next, we investigated the effect of anti-OX40L on immunosuppression in keyhole limpet hemocyanin-immunized C57BL/6J mice. In vitro treatment of anti-OX40L in CD4+ T and B220+ B cells was used to explore the role of OX40L in the pathogenesis of SLE.

Anti-OX40L alleviated murine lupus nephritis, accompanied by decreased production of anti-dsDNA and proteinuria, as well as lower frequencies of splenic T helper (Th) 1 and T-follicular helper cells (Tfh).

In keyhole limpet hemocyanin-immunized mice, decreased levels of immunoglobulins and plasmablasts were observed in the anti-OX40L group. Anti-OX40L reduced the number and area of germinal centers. Compared with the control IgG group, anti-OX40L downregulated CD4+ T-cell differentiation into Th1 and Tfh cells and upregulated CD4+ T-cell differentiation into regulatory T cells in vitro.

Furthermore, anti-OX40L inhibited toll-like receptor 7-mediated differentiation of antibody-secreting cells and antibody production through the regulation of the SPIB-BLIMP1-XBP1 axis in B cells.

These results suggest that OX40L is a promising therapeutic target for SLE. Web | PDF | Eur J Immunol. | Open Access

Edit: Aha! It was mentioned in the DecodeME candidate genes document as a potential gene that this locus applies to (thanks Evergreen for posting about this):
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In her recent talk Michelle James lists OX40 as a PET tracer that is being investigated in ME. In my email I asked if it was her or someone else studying it but I haven't heard back yet.

I know I've been a broken record about these tracers lately but it's an interesting connection!
 
V.R.T. said:
In her recent talk Michelle James lists OX40 as a PET tracer that is being investigated in ME. In my email I asked if it was her or someone else studying it but I haven't heard back yet.

I know I've been a broken record about these tracers lately but it's an interesting connection!
Click to expand...
As long as there are interesting connections there is no broken record as far as I can see. Keep them coming! Others might make a connection, somehow, somewhere.
 
Impressive work @forestglip. Can you look to see if you can find any datasets for Sjogrens. It's a CNS disease with some overlap to ME/CFS such as small fiber neuropathy.

forestglip said:
Here is that RABGAP1L region. SLE is in blue and ME/CFS is in red.
Click to expand...
This Sjogrens GWAS meta-analysis abstract also mentions RABGAP1L.
acrabstracts.org

Meta-Analysis of GWAS data from 10,003 Sjögren’s Disease Cases Identifies Thirteen Sjögren’s Risk Loci. - ACR Meeting Abstracts

Background/Purpose: Sjögren’s disease (SjD) is a systemic autoimmune condition with a complex genetic architecture. To date, 22 genome-wide significant (GWS) SjD risk loci have been identified, while >100 risk loci have been described in other related autoimmune diseases (Khatri et al. 2022)...
acrabstracts.org acrabstracts.org
 
forestglip said:
This study looks promising and has downloadable data: https://www.ebi.ac.uk/gwas/studies/GCST012796

Though it's a relatively small sample (585 cases if using just European participants). But I'll try to test the correlation with ME/CFS using this.
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I'm running into an issue with the Sjogren's data because the dataset only has the effect allele for each variant and not the reference allele, but Bigagwas requires both. So I'm not sure I'll be able to test a genetic correlation with this dataset.

I did explore the plots, looking at the significant loci from each study to see if anything similar popped out. I used the data that included all ancestries combined (1405 cases and 4747 controls). I didn't really see anything very similar.

Unlike the plot for SLE, there's nothing going on in the TNFSF4/RABGAP1L area:
decodeme-sjogren-all_chr1:170000000-177000000.png

The chromosome 6 area is interesting because of how huge the locus is for Sjogren's compared to the little ME/CFS locus on the left. (I cut off a lot of gene names below the plot because there were too many to fit). This is with only 1405 Sjogren's cases. And the locus when looking at only the 585 European cases has nearly the same significance (p=~3e-34).

decodeme-sjogren-all_chr6.png


The closest I saw to a matching locus of the ones I looked at, but it's not genome-wide significant in either disease:
decodeme-sjogren-all_chr14:44937713-46937713.png

Link to paper.
 
forestglip said:
chr6p22.2:
decodeme-sle_chr6:25739176-26739176.png
Click to expand...

jnmaciuch said:
From the blue dots, we get a clear picture of exactly where the strongest regulatory regions are upstream of the BTNs
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Sorry, my plot was probably a bit misleading on this front since it was so zoomed in. It gives quite a different picture if I zoom out to include the full SLE locus (not all genes shown):
decodeme-sle-chr6:24539176-34239176.png
 
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ChronicallyOverIt said:
A few OX40L monoclonals are coming to market soon:

Press Release: Sanofi’s amlitelimab met all primary and key secondary endpoints in the COAST 1 phase 3 study in adults and adolescents with atopic dermatitis

Sanofi’s amlitelimab met all primary and key secondary endpoints in the COAST 1 phase 3 study in adults and adolescents with atopic dermatitis Amlitelimab, dosed every four weeks or every 12 weeks,...
www.sanofi.com www.sanofi.com

"normalize the overactive immune system, without depleting T cells"

Originally for dermatitis, but I believe they think they will be good general immune T-cell suppression, possibly off-label
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This particular monoclonal is made by Sanofi, who recently agreed to let Scheibenbogen trial their CD38 inhibitor in ME/CFS. So if there is a rationale for trying their OX40 monoclonal in ME they might well be amenable.
 
mariovitali said:
@forestglip I am not sure if you have seen this analysis by Paolo Maccallini :

https://github.com/paolomaccallini-hub/MetaME?tab=readme-ov-file

I am particularly interested in genes EP300 and UGP2. Can these two be significant targets?
Click to expand...
Interesting that in this meta-analysis, there actually is a significant gene set enrichment in MAGMA: GOCC_GLUTAMATERGIC_SYNAPSE

Paolo commented this on ME/CFS Science Blog's blog about overlapping controls, so I'm not sure how much impact this may have had on the results:
There is a problem though: DecodeME and UK Biobank have overlapping controls. Now I am trying to solve this problem using a correction of the weights used in the meta analysis.
Click to expand...

I don't know enough about the method used for the meta-analysis calculation to really comment on it. But assuming it's valid, there are still 27 resulting candidate genes at the chr22 locus which includes EP300. So that could be a gene of interest, or maybe it's another of those many gene options. And there are four candidate genes at the chr2 locus that includes UGP2.
 
forestglip said:
Interesting that in this meta-analysis, there actually is a significant gene set enrichment in MAGMA: GOCC_GLUTAMATERGIC_SYNAPSE

Paolo commented this on ME/CFS Science Blog's blog about overlapping controls, so I'm not sure how much impact this may have had on the results:


I don't know enough about the method used for the meta-analysis calculation to really comment on it. But assuming it's valid, there are still 27 resulting candidate genes at the chr22 locus which includes EP300. So that could be a gene of interest, or maybe it's another of those many gene options. And there are four candidate genes at the chr2 locus that includes UGP2.
Click to expand...


Thanks @forestglip , yes I wonder if a key mechanism here is excitotoxicity via glutamate (and perhaps quinolinic acid). Another interesting finding can potentially be the gene ACO2 (exists also in the list of genes) which could be linked to the itaconate shunt hypothesis. If we have indeed issues with glutamatergic synapses coupled with ER stress and impaired ER autophagy (=ER-phagy) in the Endoplasmic reticulum then we may have a perfect storm taking place.
 
Here's an attempt to bring together all of the main candidate genes from different sources: Tier 1 genes, tier 2 genes, genes significant in MAGMA, and the gene (or two genes if it is not clear) closest to a locus for the top 25 loci.

GeneMethodCHRLead variant position (GRCh38)Reference alleleEffect alleleLead variant IDLead variant p-value
LRRC7MAGMA169696474AG1:69696474:A:G2.06E-07
xxxxx
NEGR1Nearest (1 of 2)173126414CCA1:73126414:C:CA1.19E-07
LRRIQ3Nearest (1 of 2)173126414CCA1:73126414:C:CA1.19E-07
xxxxx
ZNF644Nearest191028158CT1:91028158:C:T1.89E-07
xxxxx
RABGAP1LTier 11173846152TC1:173846152:T:C2.56E-08
DARS2Tier 1, MAGMA, Nearest1173846152TC1:173846152:T:C2.56E-08
RC3H1Tier 11173846152TC1:173846152:T:C2.56E-08
GPR52Tier 11173846152TC1:173846152:T:C2.56E-08
ZBTB37Tier 1, MAGMA1173846152TC1:173846152:T:C2.56E-08
TNFSF4Tier 11173846152TC1:173846152:T:C2.56E-08
ANKRD45Tier 11173846152TC1:173846152:T:C2.56E-08
KLHL20Tier 11173846152TC1:173846152:T:C2.56E-08
PRDX6Tier 11173846152TC1:173846152:T:C2.56E-08
SERPINC1Tier 11173846152TC1:173846152:T:C2.56E-08
SLC9C2Tier 11173846152TC1:173846152:T:C2.56E-08
xxxxx
CACNA1ENearest1181676091GA1:181676091:G:A8.85E-07
xxxxx
VRK2Nearest257808420GA2:57808420:G:A9.49E-07
xxxxx
PLCL1Nearest2197882813AG2:197882813:A:G6.64E-07
xxxxx
HTTNearest43240118CT4:3240118:C:T8.03E-07
xxxxx
ECI2Nearest64336259TC6:4336259:T:C2.90E-07
xxxxx
BTN2A2Tier 1626239176AG6:26239176:A:G4.11E-09
TRIM38Tier 1626239176AG6:26239176:A:G4.11E-09
ZNF322Tier 1626239176AG6:26239176:A:G4.11E-09
ABT1Tier 1626239176AG6:26239176:A:G4.11E-09
HFETier 1626239176AG6:26239176:A:G4.11E-09
BTN3A3Tier 1626239176AG6:26239176:A:G4.11E-09
HMGN4Tier 1626239176AG6:26239176:A:G4.11E-09
H4C8MAGMA, Nearest626239176AG6:26239176:A:G4.11E-09
xxxxx
ZNF311MAGMA629016371GA6:29016371:G:A2.25E-06
xxxxx
FBXL4Tier 2697984426CCA6:97984426:C:CA4.85E-08
POU3F2Nearest (1 of 2)697984426CCA6:97984426:C:CA4.85E-08
MMS22LNearest (1 of 2)697984426CCA6:97984426:C:CA4.85E-08
xxxxx
MLLT10Nearest (1 of 2)1021748880AG10:21748880:A:G6.34E-07
DNAJC1Nearest (1 of 2)1021748880AG10:21748880:A:G6.34E-07
xxxxx
SOX6Nearest1116217844CG11:16217844:C:G1.08E-07
xxxxx
SLC2A14Nearest127860921TA12:7860921:T:A5.79E-07
xxxxx
SUDS3Tier 1, MAGMA12118202773CTTTTTTTTTTTTTC12:118202773:CTTTTTTTTTTTTT:C1.64E-07
PEBP1Tier 112118202773CTTTTTTTTTTTTTC12:118202773:CTTTTTTTTTTTTT:C1.64E-07
VSIG10Tier 112118202773CTTTTTTTTTTTTTC12:118202773:CTTTTTTTTTTTTT:C1.64E-07
TAOK3Nearest, MAGMA12118202773CTTTTTTTTTTTTTC12:118202773:CTTTTTTTTTTTTT:C1.64E-07
xxxxx
DNAH10MAGMA12123924955GA12:123924955:G:A2.43E-07
ZNF664MAGMA12123924955GA12:123924955:G:A2.43E-07
CCDC92MAGMA12123924955GA12:123924955:G:A2.43E-07
xxxxx
OLFM4Nearest, Tier 21353194927GTG13:53194927:GT:G1.16E-07
xxxxx
PCDH17Nearest1358456743TC13:58456743:T:C9.42E-07
xxxxx
CCPG1Tier 21554866724AG15:54866724:A:G7.62E-09
UNC13CNearest1554866724AG15:54866724:A:G7.62E-09
xxxxx
SHISA6Nearest1711325637GC17:11325637:G:C8.26E-08
xxxxx
CA10Tier 1, Nearest1752183006CT17:52183006:C:T2.11E-09
xxxxx
DCCNearest1853232948CT18:53232948:C:T2.48E-07
xxxxx
CDK5RAP1Nearest2033363039GA20:33363039:G:A5.41E-07
xxxxx
CSE1LTier 1, MAGMA2048914387TTA20:48914387:T:TA9.51E-12
ARFGEF2Tier 1, MAGMA, Nearest2048914387TTA20:48914387:T:TA9.51E-12
DDX27Tier 12048914387TTA20:48914387:T:TA9.51E-12
STAU1Tier 1, MAGMA2048914387TTA20:48914387:T:TA9.51E-12
ZNFX1Tier 12048914387TTA20:48914387:T:TA9.51E-12
B4GALT5Tier 12048914387TTA20:48914387:T:TA9.51E-12
PTGISTier 12048914387TTA20:48914387:T:TA9.51E-12
xxxxx
MRPL39Nearest2125487862AAG21:25487862:A:AG5.10E-07
Click to expand...

LRRC7
NEGR1
LRRIQ3
ZNF644
RABGAP1L
DARS2
RC3H1
GPR52
ZBTB37
TNFSF4
ANKRD45
KLHL20
PRDX6
SERPINC1
SLC9C2
CACNA1E
VRK2
PLCL1
HTT
ECI2
BTN2A2
TRIM38
ZNF322
ABT1
HFE
BTN3A3
HMGN4
H4C8
ZNF311
FBXL4
POU3F2
MMS22L
MLLT10
DNAJC1
SOX6
SLC2A14
SUDS3
PEBP1
VSIG10
TAOK3
DNAH10
ZNF664
CCDC92
OLFM4
PCDH17
CCPG1
UNC13C
SHISA6
CA10
DCC
CDK5RAP1
CSE1L
ARFGEF2
DDX27
STAU1
ZNFX1
B4GALT5
PTGIS
MRPL39

Sources:
Tier 1 and 2 genes: Candidate genes document
MAGMA genes: Supplementary table 4
Top 25 loci: Supplementary table 3, with nearest gene to each lead variant found with LocusZoom. (Can also be done on UCSC Genome Browser.)

1. Go to genome browser at https://genome.ucsc.edu/cgi-bin/hgTracks

2. Press the "Hide All" button to turn off the unneeded tracks:
1761695087997.png

3. In the dropdown for GENCODE, select "pack", then press "Refresh":
1761695533139.png

4. In the search box at the top, enter a chromosomal position in the format CHROMOSOME: POSITION, then press "Search". For example, 20:48914387 is the location of the most significant variant in DecodeME:
1761695683292.png

5. Press the buttons to the right of "Zoom out" at the top of the page until you see protein coding genes show up in the main field of view, which will be colored blue. For example, I had to press the 10x zoom out button five times to see that ARFGEF2 is the nearest gene to the most significant variant:
1761696037949.png
 
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I mainly compiled the list of candidate genes above to check if any of those genes are ranked highly for rare variant associations in the Genebass browser, which includes data from a study of all the phenotypes in the UK BioBank, including ME/CFS.

The rare variant study/dataset is discussed more here: Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes, 2022, Karczewski et al

So checking all the different p-values in Genebass (SKATO p, SKAT p, and burden p for missense, pLoF, and synonymous variant associations making 9 p-values per gene) for all 59 of the above genes, these are all the genes where the p-value was below .05 in any of the tests:
GeneP-valueVariant typeStatistical test
VSIG102.53E-05synonymousBurden
VSIG103.38E-05synonymousSKATO
ANKRD450.00099synonymousSKAT
MMS22L0.00109pLoFSKAT
MMS22L0.00118pLoFSKATO
ANKRD450.00162synonymousSKATO
MMS22L0.00450pLoFBurden
ANKRD450.00585pLoFSKAT
UNC13C0.00914synonymousBurden
ANKRD450.01026pLoFSKATO
DCC0.01077pLoFSKAT
SLC2A140.01204missense|LCBurden
DCC0.01345pLoFSKATO
UNC13C0.01723synonymousSKATO
SLC2A140.01848missense|LCSKATO
TAOK30.02115pLoFSKAT
CDK5RAP10.02116synonymousSKAT
CDK5RAP10.02325synonymousSKATO
PLCL10.02359synonymousSKAT
ANKRD450.02551synonymousBurden
VSIG100.02558synonymousSKAT
RABGAP1L0.02674pLoFSKAT
ZBTB370.02692pLoFSKAT
TNFSF40.02708missense|LCSKAT
DCC0.02809pLoFBurden
CDK5RAP10.02935synonymousBurden
PLCL10.03497synonymousSKATO
ZNF6440.03584synonymousBurden
TAOK30.03714pLoFSKATO
SLC2A140.03919missense|LCSKAT
ZNF6440.03954synonymousSKAT
VSIG100.03994pLoFSKAT
ZBTB370.04032pLoFSKATO
ZNF6440.04152synonymousSKATO
TNFSF40.04163synonymousSKAT
SUDS30.04191synonymousSKAT
HTT0.04427pLoFSKAT
RABGAP1L0.04841pLoFSKATO
Click to expand...

So just maybe there's sign of a real signal for VSIG10, but otherwise, the p-values aren't really low enough to give much confidence for these genes considering how many different gene-variant type associations are being considered (59 genes x 3 variant types = 177 tests. Three times as many, 531, if considering different statistical test types on the same gene as different tests, e.g. SKAT vs SKATO).
 
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Re-reading the preprint paper, this non-scientist is struggling with how imputation was done (pooled cases+controls, versus separately ... if cases have unusual haplotypes surely separate imputation avoids forcing imputation from, essentially, population haplotypes ...).

Is it possible to tell which variants/loci were imputed - can't immediately find it in the paper/supplementary? I am sort-of thinking an imputed variant/locus is a less-reliable signal than a chip-detected SNP.
 
@forestglip, would it be worth making a locked topic with your list of main candidate genes, which only gets added to if other candidates appear?

I've once or twice found myself reading things that might be related, then having to spend ages trying to remember whereabouts in a very long thread the list of genes last appeared. It would make it easier to find for anyone who's got time and energy to do a bit of digging.

Calling it something simple like 'Main candidate genes list 2025' would make it more searchable too. I always struggle to relocate topics about papers with long titles, as it's hard to remember one word that was definitely in it.
 
arnoble said:
Re-reading the preprint paper, this non-scientist is struggling with how imputation was done (pooled cases+controls, versus separately ... if cases have unusual haplotypes surely separate imputation avoids forcing imputation from, essentially, population haplotypes ...).
Click to expand...
My shallow understanding is that imputation is done per person in a study, not pooling any participants together, by comparing that person's DNA to a whole genome reference panel, like 1000 Genomes. They look at the pattern of SNPs that they were actually able to test in a person, and see how that pattern compares to the whole genomes in the reference panel to see if a given pattern is associated with high confidence with other untested SNPs.

I wouldn't think it would cause issues for the reference panel to be healthy unlike the cases being imputed, but I'm not sure.

Whether SNPs were imputed in DecodeME is in the summary statistics files, where 1 means the SNP was actually measured, and anything less is a score for confidence about the imputation. For example:
ME/CFS Science Blog said:
Of the 8 hits, only 1 was measured, but the others have a high INFO_SCORE suggestion that their distributions follow Hardy-Weinberg Equilibrium.

View attachment 27867


I suppose it's based on the strong correlations between SNPs that you only need to know a few to be pretty certain what the others are. But I was quite surprised that the actually measured SNPs are so low (around 5% of the total, apparently).
Click to expand...
 
Kitty said:
Calling it something simple like 'Main candidate genes list 2025' would make it more searchable too. I always struggle to relocate topics about papers with long titles, as it's hard to remember one word that was definitely in it.
Click to expand...
You mean specifically genes that the DecodeME data suggests might be interesting or based on any source? Including any ME/CFS studies would be a lot more of a challenge depending on what sources are considered.

It might be useful. Some other options are linking to that gene list post from the first post of this thread, or bookmarking that post with the forum bookmark tool. What do you think? I'll also bring it up with the other mods.
 
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