Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

However if the genes are relevant and the people in the (Leeds) UK Biobank actually have ME/CFS you’d assume the genes would also show up in some way no?
I think it's possible that if their algorithm is working very differently from the method the Biobank used, the effect size wouldn't be large enough to cross the significance threshold.

Another reason CFS might not have shown up but depression did could be that there are many more cases of depression in the Biobank which would increase statistical power. Using the Biobank search tool, I see 10 times as many depression cases as CFS cases:

Chronic Fatigue Syndrome - 2,017 cases
Depression - 26,114 cases
 
This might be a silly question, but what happens if there is overlap in participants between the cohorts in genetic studies?

Could you end up trying to validate your findings on a cohort that partially includes the people you first analysed?
 
This looks too complicated for me to follow without the help of some other brains here better at this than me. I strongly suspect that there are useful data in here but it is a pity that they do not present findings in the abstract in a more transparent way. I understand what Chris Ponting is trying to do because he says so transparently. I will believe his results. I will believe these only if someone can explain to me why I should!!

But these people know what they are doing. Even if there is a bit of over-egging, I strongly suspect once we have this and the Precision Life results and DecodeME along with the Beentjes results we will start seeing what is really going on.
What is Precision Life and what is Beentjes, please?
 
I agree. And if those who know the methodology are happy that there are real associations here I think it is worth underlining how important this all is.

When the first draft of the Beentjes paper suggested that there was proof that ME/CFS was not 'psychological' (to paraphrase) I thought that might be a little premature and ill-judged. But as Chris P has himself said, what is happening now is not just about any one study. This Zhang study may be the first to hit the public domain where we can truly say there must be a biological causation because there is an identifiable genetic component. In the long run that is not what matters and I personally think that GWAS data will be easier to get my head around, but if we have reliable data that point to genetic links relevant to T cells and synapses then I think we have got a purchase on what we are looking for. And maybe NAD is in there as well from what they say.

With RA our best shot at a mechanism had 55 steps, implicating maybe 5 functional domains. We had accumulated, from various sources, genetic evidence for 3. Fitting things together enough to go ahead with treatment design did not take very long. ME/CFS may be a level more heterogeneous but I think our claim in 2016 that it was a solvable problem will turn out to be valid.
NAD as in the coenzyme Nicotinamide adenine dinucleotide, or are you referring to a gene called NAD?
 
Will the underlying dataset from DecodeME have rare variants and other data used here? Or is that not coming until SequenceME?

Presumably if it is present replication would be fairly straightforward with access to the model (a shame it hasn’t been made available)?

Or even a wider attempt to take the DecodeME data and rerun the recipe from this paper to train or just finetune the model and validate it on a larger and more consistent dataset?
What's SequenceME? I have been fairly out of the loop.
 
The chat on this paper has died down. I remain puzzled. If they have have found evidence of genetic causation in ME/CFS this is a major milestone. If everyone is unsure whether the data are statistically robust then it would be nice to have a clear idea of why.

There seems to be a problem that 'too many' gene sites have come up, making it unclear which to focus on. I wish I understood how this could happen. My intuition is that if we really think a gene is relevant then it will tell us something useful about mechanism. In the diseases I am familiar with the genetic links all make sense very easily - MHC Class I and II, PTPN22, common cytokine receptors, etc. It is such a pity that the presentation of this paper is so opaque.
 
What's SequenceME? I have been fairly out of the loop.

SequenceME is a collaboration between UoE, AfME and Oxford Nanopore Technologies which aims to conduct whole genome sequencing of 17,000 samples from the DecodeME sample set to look at every location in the 3 billion letter genome. This will go beyond the GWAS (i.e., DecodeME) by enabling identification of rare genetic variants and structural variations. More here: Oxford Nanopore, Action for ME, and University of Edinburgh launch groundbreaking study into the genetics of ME. A successful pilot phase has been completed and funding is actively being sought for the full study.
 
Just focusing on one of those, I've looked through my library for papers on neuroligin (Wikipedia) and spotted this paper that I'd squirrelled away from Jan 2024. It links neuroligin/neurexins (albeit NLGN3), autism, nitric oxide and gastrointestinal dysmotility. I've only read the abstract and it doesn't appear to have been cited thus far, but may be worth a read through the lens of ME.

Faster Gastrointestinal Transit, Reduced Small Intestinal Smooth Muscle Tone and Dysmotility in the Nlgn3R451C Mouse Model of Autism
Hosie, Suzanne; Abo-Shaban, Tanya; Mou, Kevin; Balasuriya, Gayathri K.; Mohsenipour, Mitra; Alamoudi, Mohammed U.; Filippone, Rhiannon T.; Belz, Gabrielle T.; Franks, Ashley E.; Bornstein, Joel C.; Nurgali, Kulmira; Hill-Yardin, Elisa L.

Individuals with autism often experience gastrointestinal issues but the cause is unknown. Many gene mutations that modify neuronal synapse function are associated with autism and therefore may impact the enteric nervous system that regulates gastrointestinal function. A missense mutation in the Nlgn3 gene encoding the cell adhesion protein Neuroligin-3 was identified in two brothers with autism who both experienced severe gastrointestinal dysfunction.

Mice expressing this mutation (Nlgn3R451C mice) are a well-studied preclinical model of autism and show autism-relevant characteristics, including impaired social interaction and communication, as well as repetitive behaviour. We previously showed colonic dysmotility in response to GABAergic inhibition and increased myenteric neuronal numbers in the small intestine in Nlgn3R451C mice bred on a mixed genetic background.

Here, we show that gut dysfunction is a persistent phenotype of the Nlgn3 R451C mutation in mice backcrossed onto a C57BL/6 background. We report that Nlgn3R451C mice show a 30.9% faster gastrointestinal transit (p = 0.0004) in vivo and have 6% longer small intestines (p = 0.04) compared to wild-types due to a reduction in smooth muscle tone. In Nlgn3R451C mice, we observed a decrease in resting jejunal diameter (proximal jejunum: 10.6% decrease, p = 0.02; mid: 9.8%, p = 0.04; distal: 11.5%, p = 0.009) and neurally regulated dysmotility as well as shorter durations of contractile complexes (mid: 25.6% reduction in duration, p = 0.009; distal: 30.5%, p = 0.004) in the ileum. In Nlgn3R451C mouse colons, short contractions were inhibited to a greater extent (57.2% by the GABAA antagonist, gabazine, compared to 40.6% in wild-type mice (p = 0.007). The inhibition of nitric oxide synthesis decreased the frequency of contractile complexes in the jejunum (WT p = 0.0006, Nlgn3R451C p = 0.002), but not the ileum, in both wild-type and Nlgn3R451C mice.

These findings demonstrate that changes in enteric nervous system function contribute to gastrointestinal dysmotility in mice expressing the autism-associated R451C missense mutation in the Neuroligin-3 protein.

Link | PDF (International Journal of Molecular Sciences)
 
Further on NLGN1, Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis (2023, Journal of Translational Medicine) reported the following —

We found that the CLOCK gene is significantly associated with Fatigue Dominant long COVID and ME/CFS. CLOCK (Circadian Locomotor Output Cycles Kaput) is an important regulator of circadian rhythm, disruptions of which have been associated with pain, insomnia, insulin resistance, immunological function and impaired mitochondrial function. Interestingly, one of the most common variants identified in ~ 86% of the long COVID Fatigue Dominant population mapped to the gene NLGN1. NLGN1 is also transcriptionally activated by CLOCK in the forebrain, which could indicate multiple genetic contributions to dysregulated circadian rhythm in long COVID.
 
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