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

Robert 1973 said:
Given that the study has found genetic associations, does the fact that the study used self-reported symptoms not strengthen the result?
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Yes, that was the point I was trying to make: it's diagnosis by a heatlhcare professional plus meeting IOM/CCC criteria (assessed by symptoms, inc PEM).

Robert 1973 said:
to my limited understanding, finding these associations despite the unavoidable weaknesses in the design, suggests that these signals could be stronger and more numerous if there was a way to screen patients more thoroughly (eg examined and tested by physicians as part of the study).
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Yes, and it sounds very plausible, though the reality might not have backed that up.
 
EndME said:
Good question. I think the argument largely always applies, but maybe it isn't always sensible if you can hope that there are some more specific things that make the argument meaningless (if an illness involves a red spot on the forehead then maybe if you do a GWAS on people how have this illness you'd somehow end up with some noise related to things that don't have anything to do with the red spot on the forehead but you'd also be picking up the red spot if it has a genetic signal and if it doesn't you can't but maybe you think you'd have on the basis of some noise related things)? And like you said comborbities might be one of those things.

I don't necessarily think that all confounders would necessarily have to be related to being well-defined individuals, some might be more related to getting diagnosed or participating in a study or something else that might be outside the control of the authors. I'm not quite sure if I'm being ridiculous or not, but supposedly it's possible that certain genes might make it more likely for you to be a participant in DecodeME without having to do anything with ME/CFS even if the authors tried their best to rule out such things (I think @Hutan might for example argue that female sex could possibly be one such thing).
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If all these arguments are right, I think you would expect to find a lot of common findings between chronic illness GWAS, and I don' think that is the case (though I think there is some evidence for genetic links to research participation etc).. Also, GWAS findings I'm aware of often tie in with what is already known biologically. E.g. for migraine and RA. And arguable ME/CFS, where findings pointing to immunological and neurological issues are hardly a surprise. Not least the link to infection in particular.
 
Two random thoughts
(1) Do we know eQTL locations for WASF3? Do any of these have results of interest?
(2) The gene associated with chronic pain, depending on the size of the effects, could the story be reversed to what is proposed. Ie. that chronic pain GWASes tend to include pwME and thus might pick up that effect?
 
Andy said:
The end of August marks the end of our funded period, and we will not get further funding from the MRC and NIHR. This means that almost all of our team then move on to other projects elsewhere, while Chris and a very small number of people supported by relatively small amounts of philanthropic funding continue the work.

The remaining analyses may, or may not, take up the estimated 6 months, there is no certainty, and then peer reviewed publication takes as long as the peer review process takes.
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Donate to Build on the Work of DecodeME: https://www.s4me.info/threads/donate-to-build-on-the-work-of-decodeme.45500/
 
Hutan said:
Some of the candidate genes in the supplementary document don't have eQTL information- they are the ones called Tier 2 Candidate genes.
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I think the paper explains that these are instead chosen by proximity to the lead variant. Not all genes have eQTL data, or not for all relevant tissues, and it may be that gene expression is only affected under certain circumstance - e.g. energetic demand in ME/CFS. I believe this is a thing on other illnesses too.
 
The replication with existing databases does not seem to go very well: there's likely too much uncertainty about the case definition used. So I suspect that another project with the same approach as DecodeME might be needed to confirm the results.

It will need to be in an area were ME/CFS is recognized and often diagnosed. Norway and the Netherlands are probably too small, and the US to dispersed (too few ME/CFS clinician for such a big country). So perhaps Germany would be the most likely candidate?

Overall, DecodeME wasn't that expensive given the wealth of data that it gave us.
 
ME/CFS Science Blog said:
The replication with existing databases does not seem to go very well: there's likely too much uncertainty about the case definition used. So I suspect that another project with the same approach as DecodeME might be needed to confirm the results.

It will need to be in an area were ME/CFS is recognized and often diagnosed. Norway and the Netherlands are probably too small, and the US to dispersed (too few ME/CFS clinician for such a big country). So perhaps Germany would be the most likely candidate?

Overall, DecodeME wasn't that expensive given the wealth of data that it gave us.
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Iirc they are actually planning a GWAS with Lipkins lab
 
On replicatiion, would it have been useful to exclude, say, 10% of the patient samples from the initial GWAS, and then use those samples to try to replicate only the genetic signals that reached statistical significance? Wasn’t something like that done in a previous study?

Am I right in thinking that if another study wanted to try to replicate only the 8 signals that were identified in this study and not look for other signals it would be possible to get statistical significance with a much smaller sample size?
 
Robert 1973 said:
On replicatiion, would it have been useful to exclude, say, 10% of the patient samples from the initial GWAS, and then use those samples to try to replicate only the genetic signals that reached statistical significance? Wasn’t something like that done in a previous study?
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It's a risk though. There might only have been significant hits just on the cusp of the threshold with the full sample. Reducing the sample size could lead to losing those findings. It might be better to put all the power possible in it while we've got it, and let the replication happen later.
 
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