Utsikt
Senior Member (Voting Rights)
Why would ‘most hits’ be the criteria to judge diagnostic criteria on?
Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration
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.
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.
Hoopoe
Senior Member (Voting Rights)
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.
But why? People without the risk genes for MS have MS just like those with the risk gene?
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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Yann04
Senior Member (Voting Rights)
Given the data heritability estimate is 9.5% I guess it could give us “max” a 9.5% better chance?
Yann04
Senior Member (Voting Rights)
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.
Yeah, I realized after posting that with a small heritability it might still not be very useful.
Yann04
Senior Member (Voting Rights)
It could be useful for testing the findings no right? Like if in other ME cohorts it has a prediction value of >5%, that might help confirm decode ME is generalisable?
Hoopoe
Senior Member (Voting Rights)
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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Yeah, good point. Still good information one way or another.
Yes but how would that work? If you'd just include people with MS, or Ank Spond in your ME/CFS cohort you might artificially just get more significant findings but your diagnostic criteria to get there will mean that you're not actually doing anything meaningful anymore. Maybe a certain Fukuda cohort ends up having many hits due to MS etc.
DecodeME showed that you can just ask people whether they have ME/CFS (edit: in the sense as mentioned by Andi below) and actually get some genes from that. But it isn't clear whether that is applicable to other cohorts and to what extent (the failure to replicate suggests it won't work for badly selected cohorts but we don't know what the results will look like in clinical cohorts or say a "German DecodeME cohort"). I think a GWAS in a country including a clincal cohort and a DecodeME like cohort could be a useful next step to understand how the signals behave then.
I think it should be fairly simply to do a grading how symptoms correlated to significant gene presence, but I don't think that's necessarily meaningful for identifying diagnostic criteria.
The problem here would be the same as always: Against what value do you define high accuracy of disease? Every criteria is 100% accurate against itself and we don't know any diseases processes as of yet.
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Andy
Senior Member (Voting rights)
But that isn't what we did. We asked our participants to confirm that they had a diagnosis from a medical professional AND we put them through a screening questionnaire to assess them against CCC and IOM criteria.
Given that we have confirmed that this method has produced a genetically reasonably well defined cohort I don't see much value in deviating from that method.
Jonathan Edwards
Senior Member (Voting Rights)
Yes, that is what I meant. What I meant with a "German DecodeME cohort" would be to just repeat this procedure (supposedly without telling them that you're doing this) and also have a clinical cohort as comparison (that also undergoes the questionnaire screening).
Kitty
Senior Member (Voting Rights)
But if they were treated as one disease because there was no practical way to distinguish them, I guess the drug would tell you a good bit about where not to look for an answer for the non-responders?
Simon M
Senior Member (Voting Rights)
Thanks, both. That have been my understanding, but I wanted to check it.
ADDED and I think this is an important message to convey to people who understandably wonder what the relevance of this big genetic study is to them when many won't have the relevant genetic differences, and heritability is so low.
I know my blog didn't address this
Simon M
Senior Member (Voting Rights)
I would say the gold standard is diagnosis by expert clinicians. We know from a couple of studies that half of GP diagnoses are wrong – and almost all cases because that had been other, undiagnosed diseases (mostly biomedical, but also psychological) that explain the symptoms.
I suspect the specific criteria are less important, so long as we have Pem. (I'll be interesting to see, though, how.DecodeME cases would stack up against expert diagnosis as they, probably uniquely, have a decent definition of PEM, which does seem to be a very unusual symptom).
By expert clinicians, I mean those who are very familiar with the illness, and I will resource. The two papers that found about 50% of GP diagnosis were were led by Julian Newton and Peter White respectively. Plus, I think they had bigger teams for this to do a good job.
GP's unfamiliar with the illness and often dismiss it. Even specialist clinics are frequently held up by lack of resources, or by impeded by beliefs.
I believe that DecodeME is the best large cohort we have available, and the best possible job that could be done.
But it would still miss cases where there are undiagnosed alternative diagnosis. That's why I think it's use of a good pen question is so important.
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mariovitali
Senior Member (Voting Rights)
I am sharing the initial results from the analysis I have been performing using 5 different reasoning engines to suggest causal hypotheses given the GWAS results and the candidate genes. @Hutan it was difficult to extract text because of the table.
I will not provide the details on how it was done here, I am providing this information in hope that helps experts to formulate their hypotheses.
Tagging @Chris Ponting @DMissa @Jonathan Edwards
EDIT : The hypotheses generated are not discussed here but on a later post below.
First the areas of agreement in the 5 hypotheses generated by the reasoning engines :
And here are the areas of disagreement.
I will not provide the details on how it was done here, I am providing this information in hope that helps experts to formulate their hypotheses.
Tagging @Chris Ponting @DMissa @Jonathan Edwards
EDIT : The hypotheses generated are not discussed here but on a later post below.
First the areas of agreement in the 5 hypotheses generated by the reasoning engines :
And here are the areas of disagreement.
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Jonathan Edwards
Senior Member (Voting Rights)
Hi @mariovitali ,
To me those responses are just a rehash of all the trendy stuff constantly recycled by people thinking inside a box with no real understanding of global biodynamics. There is no actual explanatory model, just buzzwords.
I think asking @jnmaciuch or @Snow Leopard and a few others here, some with little or no technical training in the field but a good dose of common sense, is likely to provide a more interesting answer.
To me those responses are just a rehash of all the trendy stuff constantly recycled by people thinking inside a box with no real understanding of global biodynamics. There is no actual explanatory model, just buzzwords.
I think asking @jnmaciuch or @Snow Leopard and a few others here, some with little or no technical training in the field but a good dose of common sense, is likely to provide a more interesting answer.