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

Hutan said:
So, it's a bit puzzling. I'm assuming the sex chromosome results weren't prioritised for inclusion in the initial preprint for a strategic reason. I'm not sure what the reason is. @Andy, Simon M?
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I suspect it is it's practical rather than strategic. I believe sex chromosomes have to be analysed separately in all GWAS . So it makes sense to do all the autodomed (non--sex chromosomes) first.

The wind we also have six chromosome data – which is one of the things they flagged up – we'll have a better understanding of why there is such a big sex difference in diagnosed cases of ME/CFS.
 
Simon M said:
Interesting. This came out in 2023, and I was unsurprised then, and I don't think it drew any comment from PwME. I'm sure I've seen similarly high figures in other large patient surveys. Can anyone point to other large symptom surveys?

Assuming this is right, the surveys are more likely to be representative of the broader population than either forums or webinars (I think there are only 400-odd members here, for instance).

Pain is a major feature of my PEM. I had assumed it was for many (though I don't think I have see PEM-pain covered in surveys).
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This is from the MEA's 2010 survey:
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I saw that the rate of muscle pain was mentioned (presumably because it was the highest) but didn't find data on the other pain questions (for example the 2 joint pain questions). Presumably this will still follow at some point in time or the data can be requested, but having this data at some point would probably be nice. There's some additional data in the previous study (Typing myalgic encephalomyelitis by infection at onset: A DecodeME study), but I find it a little bit harder to make out what the pain data exactly means.

(My layman impression is that joint pain is more common in the general population than muscle pain, especially when people get older, so having some idea how "ME/CFS specific" things are, would be nice).
 
Chris Ponting said:
this variation highlights what biological aspects need changing via drugs. And these drugs can be made to alter biology to a far, far greater extent than the genes can.
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(This as the answer to the question about how can small genetic differences between cases controls produce valuable information )

Is it also true that somebody who doesn't have a relevant genetic difference in a particular gene can benefit from any such drug targeting the biology behind the gene?

In other words, can information from genetic study help all those people with the illness who don't have relevant genetic differences?
 
ME/CFS Science Blog said:
In an interview with David Tuller, Ponting also said something interesting (starting at minute 21:23):
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Ponting: What we are doing is asking the question whether the associations we are seeing are equivalent to what others have seen for other diseases. And thus far the ME ones seem to be specific to ME and not to any other traits or disease aside from this one on chronic pain.

Tuller: And that means what?

Ponting: It means that the ME genetic signals are not equivalent to any… arthritis, Parkinson’s, Alzheimer’s, depression, anxiety, none of those and more.
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Andy said:
All I can tell you is this from the FAQs.

"Are these findings unique to ME or have they been found in other illnesses?

The signals we have found are different from those found in other illnesses to date, except for the one on chromosome 17 that was previously found in people experiencing chronic pain."
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This is interesting. But I don't see the preprint talk about the data source for looking for shared intervals with all these other assorted traits like Parkinson's and arthritis. Only depression, pain, and anxiety. @Chris Ponting, is it that all traits in the UK BioBank were checked for similar significant intervals?
 
Simon M said:
In other words, can information from genetic study help all those people with the illness who don't have relevant genetic differences?
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I've been trying to understand this through a made-up analogy. Thought I might share it to see if it holds and if others find it useful or not.

Suppose an illness is caused by a structure somewhere in the body that lets cells through that it should hold back, like a dam that is breaking. There is one gene X that helps to create a simple protein that acts as one of many support structures in the dam.

One variant of the gene creates a slightly stronger protein than the other variant to support the dam. The difference between the two proteins is minor and this protein is only a minuscule part of what holds the dam. There are many other mechanisms involved in the strength of the dam that involves feedback loops and complex interactions. These are much more important but gene X is simple and straightforward. It only has one job.

In GWAS of the illness, gene X might show up with a very small effect size. The others don't show up because the mechanisms are too complex and intertwined or there is a signal but it's ambiguous and hard to interpret. Luckily gene X points to the problem: the dam is breaking! And luckily scientists know a lot of biology so that they can do much more to support the dam than gene X could by coding its protein. They can create drugs that ensure the dam no longer breaks.

So in this analogy, the dam might be breaking even in those with the stronger protein from gene X. And fixing the dam might cause physiological changes and benefits that are out of proportion to the effect size of gene X.

Now I only hope that real life works like this as well!
 
Exactly @ME/CFS Science Blog. Any of a hundred genes can lead us to a treatment strategy that is not dependent on any of them. Ritux for RA works whether or not you have DR4, but DR4 pointed us to it.

Everyone has a DR gene of course. And it is involved in the whole scenario that provides the causation. And all genes do to a degree, for all diseases, but the critical step that flips into RA does not need the DR4 version.

These gene variants are just clues to a testable theory. Just as the pump was the clue to all cholera, even where there are no pumps.
 
I guess the caveat is that MECFS might be two similar looking diseases like DR4 encouraged arthritis and B27 encouraged arthritis. Ritux is no good for the latter. But it was knowing about both genes' significance that allowed us to develop treatments for both when the technology came along. We knew not to give up on anti-IL17 just because it didn't work for RA.
 
It means that the ME genetic signals are not equivalent to any… arthritis, Parkinson’s, Alzheimer’s, depression, anxiety, none of those and more.
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I'm wondering if it's possible to use a GWAS to develop better diagnostic criteria for diseases with no objective tests and unspecific symptoms.

One could use GWAS data to compare different diagnostic criteria or combinations of different parameters to see which ones give the most hits above the significance threshold. One could go a step further and compare the profile (a list of the genomic regions of the hits) with that of other diseases which are difficult to separate. Maybe it's possible to find patterns in the data that suggest the disease being studied is actually more than one disease.

Has something like this been attempted?
 
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Hoopoe said:
I'm wondering if it's possible to use a GWAS to develop better diagnostic criteria for diseases with no objective tests and unspecific symptoms.

One could use GWAS data to compare different diagnostic criteria or combinations of different parameters to see which ones give the most hits above the significance threshold. One could go a step further and compare the profile (a list of the genomic regions of the hits) with that of other diseases which are difficult to separate. Maybe it's possible to find patterns in the data that suggest the disease being studied is actually more than one disease.

Has something like this been attempted?
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Why would ‘most hits’ be the criteria to judge diagnostic criteria on?
 
Unrelated to above discussion:

The individual variants aren't going to be diagnostically useful from this study. But I wonder if there might be an attempt to make a polygenic risk score from the DecodeME data and then see how well it classifies patients in other databases.
 
Utsikt said:
Why would ‘most hits’ be the criteria to judge diagnostic criteria on?
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I didn't say it was the criteria that should be used. It's just a possibility. It should be possible to devise some criteria that fulfills the purpose.

I'm thinking that the 8 hits in DecodeME is a powerful sign that there is at least one specific disease whose signals the study was able to capture. It validates the diagnostic criteria, recruitment process and the analysis. It's not just the number of hits, but also how they fit together in a coherent way and don't resemble some other disease.
 
Hoopoe said:
Maybe it's possible to find patterns in the data that suggest the disease being studied is actually more than one disease.
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Doesn't this already happen? Look at risk genes (or location) and look at spread across the study population. The easiest example would be where you have 2 extremely significant risk genes and see that people on average only have 1, suggesting that there may be 2 different underlying pathways (unless the genes somehow end up having the same function). But here the situation will be a lot harder, but I did think people would look at this.
 
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forestglip said:
Unrelated to above discussion:

The individual variants aren't going to be diagnostically useful from this study. But I wonder if there might be an attempt to make a polygenic risk score from the DecodeME data and then see how well it classifies patients in other databases.
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Given the data heritability estimate is 9.5% I guess it could give us “max” a 9.5% better chance?
 
EndME said:
But why? People without the risk genes for MS have MS just like those with the risk gene?
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People talk about stricter research criteria to try maximise the chances of finding significant findings that then may also apply to the community at large. Ie. the (problematic but also interesting idea) ICC for ME/CFS.
 
EndME said:
But why? People without the risk genes for MS have MS just like those with the risk gene?
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In ME/CFS we have the problem that we don't know what diagnostic criteria are best.

We can create different diagnostic criteria according to what we believe best describes the disease, but that's just an opinion-based process.

If we had an objective method for defining ME/CFS, then we could develop and refine diagnostic criteria until they could capture the disease with high accuracy.

I'm wondering if a GWAS can be used to provide some much needed objectivity and help validate or refine the diagnostic criteria, or discover subtypes.

Immagine if we could have some objective data showing that with one diagnostic criteria, you get few hits with significant overlap with other disease, while with another, you get more hits and less overlap.
 
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