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

[forestglip]

Is there currently ANY whole genome data for ME? Is SequenceME the only WGS project on the horizon?

I think just much smaller studies or studies that didn't use as strict of definitions. For example, the UK BioBank has WGS data for their chronic fatigue syndrome phenotype, and it was included in a rare variant study, but the cohort is smaller than DecodeME and the definition of CFS is more permissive.

There's this study on only 20 cases of severe ME/CFS: A Network Medicine Approach to Investigating ME/CFS Pathogenesis in Severely Ill Patients: A Pilot Study, 2024, Hung, Davis, Xiao

This study included a few hundred cases with WGS data obtained from three cohorts (Stanford, CureME, and Cornell): Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis

Just the ones I can think of right now, there might be more. I'm not sure about other future projects.

 

[arnoble]

There's this study on only 20 cases of severe ME/CFS: A Network Medicine Approach to Investigating ME/CFS Pathogenesis in Severely Ill Patients: A Pilot Study, 2024, Hung, Davis, Xiao

Seems like the 9 genes they identified ACADL, BRCA1, CFTR, COX10, HABP2, MFRP, PCLO, PRKN, and ZFPM2 do not show up in DecodeME.

This study included a few hundred cases with WGS data obtained from three cohorts (Stanford, CureME, and Cornell): Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis

I WILL look at this (and the 24 pages of comments), but might you have a TLDR?

Thanks so much for your help - all your hard efforts are obviously appreciated by many many s4me folks, and I just wanted to add my appreciation.

 

[forestglip]

I WILL look at this (and the 24 pages of comments), but might you have a TLDR?

Quick and potentially not totally accurate summary: They created a machine learning model designed to predict whether participants were cases or controls based on rare variants in their DNA. The prediction algorithm was connected to an external database of known protein interactions (STRING) which influenced the training of the model.

I think it went something like: if cases tend to have variants in a certain gene (gene A), then the model will train to also look for variants in other related genes (related based on STRING database) for later predictions, based on the assumption that if cases have variants in gene A, and if gene A is highly related to gene B, then future cases are also more likely to also have variants in B, even if the specific cases that were tested did not necessarily have any variants in B.

After training, the researchers looked at the model weights (the inner workings of the trained model) to see which genes it prioritized for making predictions. In the paper they highlighted the 115 most highly prioritized genes as being the "ME/CFS genes" (genes copied to this post). But the limitation above applies that some of these high priority genes might not have actually had many rare variants in the cases tested, so some of these genes may or may not actually be relevant to ME/CFS.

Then they looked to see what kinds of processes these 115 genes (or subsets of them) tend to be involved in, and found that these genes seem to be involved in several areas, with the two main ones highlighted in the paper being synaptic function and proteasome function.

 

[Tree]

Have not read whole thread, apologies.

Question; when will Decode ME go from a preprint to a definite publication?

 

[Andy]

Have not read whole thread, apologies.

Question; when will Decode ME go from a preprint to a definite publication?

Once the ongoing remaining analyses are complete. Current guess when that might be? Some time next year, dependent on the peer review process.

 

[Jonathan Edwards]

Question; when will Decode ME go from a preprint to a definite publication?

I don't think this matters. Formal publication used to ensure wide dissemination and that the study was as well written up as could be expected. Those are already sorted for studies like this.

 

[Robert 1973]

Once the ongoing remaining analyses are complete. Current guess when that might be? Some time next year, dependent on the peer review process.

Do you anticipate that any of ongoing analysis will be made available before it is formally published in a peer reviewed journal?

 

[Andy]

Do you anticipate that any of ongoing analysis will be made available before it is formally published in a peer reviewed journal?

Good question and something that hasn't been discussed internally yet. Personally, unless there were to be good reasons not to, I would be in favour of publishing a revised version of the current preprint, adding the results of the additional analyses, before submitting it for peer review.

 

[arnoble]

I think it went something like:

You should be writing these papers - what a clear and crisp summary. I am very grateful - you have inspired me to now start a close reading of the paper and comments. Thanks again.

 

[Dolphin]

Update VHS conference 2025: DecodeME poster finalist.

Go to,
https://vhscotland.org.uk/vhs-poster-competition-finalists/
https://vhscotland.org.uk/wp-conten...VHS-conference-poster-Oct-2025.jpg-scaled.jpg

 

[forestglip]

We have replicated data relating to association with EBV and several other infections. We have what looks like a reliable estimate of genetic causation (~10%), with replication other than in one or two outlying studies.

Responding to this from another thread.

Relevant part of DecodeME paper:

We estimated ME/CFS SNP-based heritability from GWAS-1, based on the LDSC method and reported on a liability scale. It was modest but significantly different from zero, with ℎ 2 = 0.095 (SD = 0.006).

So my understanding is that SNP-based heritability, as reported above, only captures the genetic influence from what was measured in the study: common variants. That means it misses any genetic influence of rare variants. And I don't think it captures structural changes like copy number variants and inversions.

Also, I think GWAS tests assume a certain model, which I think is most commonly the additive model, meaning having one copy of the risk allele increases risk by some amount and having two alleles doubles the risk. As opposed to a dominance model, where having one or two alleles causes equal increases in risk, or a recessive model, where only having both risk alleles can increase risk. Regenie documentation says you can choose the model to use. If you set it to additive, I think you would miss a lot of the influence from recessive or dominant mutations.

Here is an image from a paper visualizing the genetic contributions to autism spectrum disorder (ASD), schizophrenia (SCZ), and Alzheimer's disease (AD). For autism, for example, the genetic heritability is estimated at 82% of total liability (liability includes genetic and environmental influences). Yet the portion based on common variants is only around 12%. There's also some heritability due to rare variants, non-additive variants, plus 59% of liability is genetic heritability which has not been attributed to a specific factor.

From variants to mechanisms: Neurogenomics in the post-GWAS era, 2025, Neuron



I think total genetic heritability will be better captured by family studies, though I'm not sure how exactly they disentangle shared environment.

 

[Jonathan Edwards]

So my understanding is that SNP-based heritability, as reported above, only captures the genetic influence from what was measured in the study: common variants.

Yes, that is my understanding. However, my memory is that other data fit with ~10% and Chris gave the impression (I thought) that there were reasons to think this was most of the risk. My guess is that if genes are really rare they don't make much difference and if they aren't rare then it is a bit unfair if none of them show up on the GWAS, but others will know more than I do. I would be surprised if the h2 value was above 20%. Which means we have enough of a ball park estimate to guide both theory building and clinical advice.

 

[Jonathan Edwards]

I don't actually know where sex ratio figures in h2 estimates - whether it is hived off as a separate issue.

 

[forestglip]

However, my memory is that other data fit with ~10% and Chris gave the impression (I thought) that there were reasons to think this was most of the risk.

My impression was that we don't really have good total heritability data. Snippet from 2020 paper from Dibble, Ponting, and McGrath:

Genetic risk factors of ME/CFS: a critical review, 2020, Human Molecular Genetics

Of three studies that have estimated narrow-sense heritability (h2) using large cohorts, two reported non-zero h2-values that provide evidence for heritability of risk for CFS and, presumably, ME/CFS.

An analysis of US health insurance claimed a high narrow-sense heritability (⁠⁠h2 =0.48) of CFS (23),

whereas an analysis of the UK Biobank individuals self-reporting a CFS diagnosis reported a less striking heritability (single nucleotide polymorphism- [SNP-] based approximate h2 = 0.08 with low confidence) (24) (http://www.nealelab.is/uk-biobank).

The third, a large twin-based study of CFS-like cases, produced an inconclusive result, with the 95% confidence interval of h2 including zero [0.03 (0.00–0.65)] (25).

So only two of these were not inconclusive. One of these (UK Biobank, 8%) is only SNP-based heritability, like DecodeME's figure. I believe the 48% from the insurance study would include more forms of genetic heritability, though I don't know anything about the study or patient selection.

My guess is that if genes are really rare they don't make much difference and if they aren't rare then it is a bit unfair if none of them show up on the GWAS, but others will know more than I do.

Not saying I know much either, and I hope an expert can weigh in. But in the image I shared it says very large proportions of heritability are as yet unattributed in all three diseases, larger than the amount determined from the common variants in GWAS. (Though looking at the figure, the lengths of the bars are kind of wonky. In AD, the 32% bar for unattributed risk is longer than the 35% bar for non-APOE common variants.)

I'm not sure why it'd be unfair if it didn't show up on a GWAS. There might be recessive and dominant alleles that contribute to risk. There might be copy number variants. The GWAS is only testing some forms of genetic risk.

 

[Jonathan Edwards]

My impression was that we don't really have good total heritability data.

It's a bit of a mess, yes, but I read it like this:
DecodeME points to 9.5%+ .
The Biobank data at 8%+ is similar.
The insurance twin study doesn't look plausible. If h2 was 48% I think that would have been clinically obvious. It is a bit hard to know why this one should be so out of line but I am inclined to either ignore it or allow that the others may be missing some rare stuff. But then the other twin study is even lower at 0-6%. That also seems a bit implausible since we do see multicase families more often than by chance it seems. But if it was 6% it wouldn't be so very far out of line.

Maybe we have to allow for 10-25% being plausible but my memory from the diseases I worked with is that rare genes don't add that much. RA is mostly XX, DRbeta and PTPN22.

 

[forestglip]

But then the other twin study is even lower at 0-6%.

Noting that it was 0-65%.

 

[Jonathan Edwards]

Noting that it was 0-65%.

Ah, that's a confusing figure. So it is a best fit of 3% with very wide uncertainty, including 10-25%!
I still go for 10-25%.

 

[Dolphin]

From Facebook:

Conference presentation now available on YouTube!

This week we shared our work on Patient and Public Involvement within DecodeME at The International Conference on Clinical and Scientific Advances on ME and long Covid, held in Porto, Portugal.

Sian, an Action for ME staff member who is also a member of the PPI steering group for DecodeME and PPI Coordinator for the PRIME project, presented as part of the 'Patient Advocacy Panel’ session.

Watch the recording of this presentation on YouTube:

#MECFS #pwME #MyalgicE #MyalgicEncephalomyelitis

 

[Kitty]

That's really nicely done, thank you Sian and team!