Some discussion about variants near SUDS3/TAOK3 and their possible relationship to lupus have been moved to: Genetics: Chromosome 12: SUDS3, TAOK3
Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration
mariovitali
Senior Member (Voting Rights)
@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?
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?
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.
mariovitali
Senior Member (Voting Rights)
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.
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.)
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
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:
3. In the dropdown for GENCODE, select "pack", then press "Refresh":
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:
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:
2. Press the "Hide All" button to turn off the unneeded tracks:
3. In the dropdown for GENCODE, select "pack", then press "Refresh":
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:
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:
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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:
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).
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:
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.
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.
Kitty
Senior Member (Voting Rights)
@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.
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.
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:
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.
Kitty
Senior Member (Voting Rights)
I just meant that list you made in post #793.
@Kitty, here is that thread: https://www.s4me.info/threads/main-candidate-genes-from-decodeme-2025.46949/
Brilliant - I never would have found that.
For example, if ALL pathological ME variants are absent on the UKB Axiom array, how sure can we be that imputation against the general population will cause them to be associated with pwME?
I don't think I understand imputation well enough to answer. In any case, we know there is a lot of the genome data missing in DecodeME. Imputation can only get you so far, and some of the imputed variants might be wrong.
A whole genome sequencing study, AKA SequenceME, would be very valuable so that we could actually measure all the positions instead of making educated guesses about all the non-measured positions.
Jonathan Edwards
Senior Member (Voting Rights)
My impression is that 'imputation' is used to cover more than one inferential process, at least one involving individual SNP profiles (imputing intervening SNPs) and another making use of population weightings of SNP forms (imputing risk genes from minihaplotypes of SNPs).
On the TNFSF4 (also known as OX40L):
a paper on lupus (SLE) quoted by Forestglip said that an up-regulation of TNFSF4/OX40L (the OX40 ligand) predisposes to lupus. I think TNFSF4 activates CD4+ t-cells:
BUT DecodeME seems to be suggesting the opposite for ME/CFS - a decreased expression of TNFSF4, at least in some defined tissues
Members have noted that some drugs that reduce expression of TNFSF4 are coming to market and there was a suggestion that it could be tried in ME/CFS.
If I have understood things correctly, (and I may well not have, in which case, let me know), knocking down expression of TNFSF4 in people with ME/CFS would be very unlikely to help. The genetic variant found by DecodeME that reduces TNFSF4 expression might only be relevant to increasing the risk of developing ME/CFS, so an OXO40L monoclonal that reduces TNFSF4 expression might not make symptoms worse, but there doesn't seem to be a good rationale to try it.
?
a paper on lupus (SLE) quoted by Forestglip said that an up-regulation of TNFSF4/OX40L (the OX40 ligand) predisposes to lupus. I think TNFSF4 activates CD4+ t-cells:
Another quoted paper suggests that blocking the ligand and receptor can improve lupus:
BUT DecodeME seems to be suggesting the opposite for ME/CFS - a decreased expression of TNFSF4, at least in some defined tissues
Members have noted that some drugs that reduce expression of TNFSF4 are coming to market and there was a suggestion that it could be tried in ME/CFS.
If I have understood things correctly, (and I may well not have, in which case, let me know), knocking down expression of TNFSF4 in people with ME/CFS would be very unlikely to help. The genetic variant found by DecodeME that reduces TNFSF4 expression might only be relevant to increasing the risk of developing ME/CFS, so an OXO40L monoclonal that reduces TNFSF4 expression might not make symptoms worse, but there doesn't seem to be a good rationale to try it.
?
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I think there are often situations where the same variant can be associated with increases or decreases of gene expression depending on the tissue, or maybe depending on other factors like age.
For example, a variant from a past study:
Apart from that, the gene expression database isn't comprehensive of every tissue that might be affected, both because there might be too low of statistical power and because not every possible cell type/tissue type is included in the database.
So just noting that it's possible this variant might lead to increased expression where it matters for ME/CFS. Or of course you might be right and it could be a dead end.
V.R.T.
Senior Member (Voting Rights)
So I may be misremembering but the Sanofi OX40L monoclonal was claimed by the company to have a t cell calming/soothing effect, and JE and others have theorised T Cells may be central to the mechinism of MECFS. So I was going from that and the connections I saw iirc.
But i didnt realise it was down not up in DecodeME.
Kitty
Senior Member (Voting Rights)
Thank you! Bookmarked.
I have spent an hour or two grappling with phasing and imputation, but realize the task is very large and complex. It just bothers me that most of the SNPs in the preprint are inferred by fiendish arithmetic.
As you say, SequenceME will be a blessing. Is there currently ANY whole genome data for ME? Is SequenceME the only WGS project on the horizon?