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

forestglip said:
Oh, so "C2_v2_england_controls" means "COVID-19 positive (controls include untested), only patients from centers in England". So they had a COVID illness but not everyone was confirmed with a lab test? I guess that means both groups were COVID positive and having both associations makes sense.
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On looking again, I realized I misunderstood this.

C2_v2
COVID-19 positive (controls include untested)

C2_v2_england_controls
COVID-19 positive (controls include untested), only patients from centers in England

There's nothing different about the groups in terms of lab testing, the second one is the same thing, just only including participants in England. I got confused because "controls" got added to the code for the England group for some reason.

So both groups include the cases group where people tested positive for COVID and the control group where people either tested negative or didn't get tested.
 
forestglip said:
Considering the similarities between ME/CFS and depression, and how the distinction for accurate diagnosis can be tricky,
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On the one hand I’ve had both at different times and found they’re very very different conditions, almost polar opposites in some ways. So I find people talking about similarities difficult to understand.

On the other I’ve had both at different times so there may be a genetic predisposition.

It’s certainly an interesting one.

forestglip said:
So both groups include the cases group where people tested positive for COVID and the control group where people either tested negative or didn't get tested.
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Thanks for getting your head around this on our behalf! I found that whole chunk of the paper confusing tbh. I get their goal but am unsure if it tells us much. There could just be lots of people in the UK Biobank flagged in their records as having had depression or covid, both very high prevalence conditions over time (and with very wide ranging severity).
 
hotblack said:
On the one hand I’ve had both at different times and found they’re very very different conditions, almost polar opposites in some ways. So I find people talking about similarities difficult to understand.
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I was depressed when I got covid and ME/CFS as a result of that.

To me, there are no similarities at all, other than reduced activity in general. I got help with my depression during the first year of being ill, and it ‘s night and day.
 
hotblack said:
On the one hand I’ve had both at different times and found they’re very very different conditions, almost polar opposites in some ways. So I find people talking about similarities difficult to understand.
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Utsikt said:
To me, there are no similarities at all, other than reduced activity in general. I got help with my depression during the first year of being ill, and it ‘s night and day.
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But would you not expect that a decent chunk of the people in the depression group in the BioBank might have ME/CFS? It might feel different when you know the differences, but if a patient has ME/CFS but they or their doctor don't know about that condition, I think many people would assume they have depression instead, mainly based on the drastically reduced activity levels.
 
forestglip said:
But would you not expect that a decent chunk of the people in the depression group in the BioBank might have ME/CFS? It might feel different when you know the differences, but if a patient has ME/CFS but they or their doctor don't know about that condition, I think many people would assume they have depression instead, mainly based on the drastically reduced activity levels.
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Yes, there is overlap because depression questionnaires are quite bad and would flag many normal traits for sick people as depression.

I think we were talking about the experience of actually being depressed vs having ME/CFS.
 
Yes, @Utsikt puts it well.

I agree there may be some crossover in groups but that is generally a failure of those giving the diagnosis. To me there are not similarities in the illnesses but misunderstanding and poor diagnosis.

When I was depressed I was physically able to do things but didn’t see the point and to be honest would have been fine with my life just stopping.

Now I have ME/CFS I am physically unable to do things but really want to and am fighting to make the most of life and one day have more of it back.

In my view/experience these are trivial for anyone with an understanding of both conditions to differentiate between. Probably with a few simple questions.

I’m simplifying a bit and there are crossovers and complexities, people fan have both, but that they are often seen as similar is a I think a failure to understand either condition by medical practitioners.
 
hotblack said:
Yes, @Utsikt puts it well.

I agree there may be some crossover in groups but that is generally a failure of those giving the diagnosis. To me there are not similarities in the illnesses but misunderstanding and poor diagnosis.

When I was depressed I was physically able to do things but didn’t see the point and to be honest would have been fine with my life just stopping.

Now I have ME/CFS I am physically unable to do things but really want to and am fighting to make the most of life and one day have more of it back.

In my view/experience these are trivial for anyone with an understanding of both conditions to differentiate between. Probably with a few simple questions.

I’m simplifying a bit and there are crossovers and complexities, people fan have both, but that they are often seen as similar is a I think a failure to understand either condition by medical practitioners.
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I have the same experience. And I’ve also got the assessment of a therapist that followed me the first 2.5 years after getting sick.

She was adamant that there is nothing wrong with my mental health now, yet I would have scored high on most surveys.

That’s the issue with most psych labels - they don’t consider the circumstances or alternative explanations for the results.
 
On the statistical point, given depression is reported by what, 20-25% of people? Maybe more over a lifetime. And IBS is very common and almost everyone got covid, the associations with them just seem very sketchy to me. It’s like looking for a correlation between people that had lunch today and people with these gene variants.

Maybe I’m being overly cynical or missed something they did to factor this in but it seems like a stretch to me to layer this on top of their earlier findings. It detracts rather than adds IMHO.
 
Sasha said:
It's been a week since you submitted your comment and it's still not up. :(
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Sorry, I had actually deleted the comment after the author replied by email the first time. (No second reply yet but I followed up.) But I made a new comment on MedRxiv yesterday (still in moderation) since a response on there would probably be better so that anyone who views the preprint can see the genes.

Edit: I removed the MedRxiv comment to not annoy the authors. If they can share it, they'll get back to me by email.
 
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forestglip said:
Sorry, I had actually deleted the comment after the author replied by email the first time. (No second reply yet but I followed up.) But I made a new comment on MedRxiv yesterday (still in moderation) since a response on there would probably be better so that anyone who views the preprint can see the genes.
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Sorry, I haven't been following the thread! Do you feel we're further forward with understanding what they found and what its implications are?
 
The authors have sent me Supplementary Table 2 and allowed me to share it here. They also informed me that they've uploaded it (and I assume the other tables) to MedRxiv, but it'll take some time for it to be made visible. I attached the spreadsheet to this post which appears to have every gene they tested, but here are the top 115 that were used in the model:

Gene | p_value | q_value | Attention_difference_case_vs_control
DNMT3A | 9.827243889E-09 | 0.00002300778546 | 0.6657754794
ADCY10 | 0.0000004241863691 | 0.000496557788 | 0.3852176317
PPP2R2A | 0.000006694100941 | 0.005224131339 | 0.4087916389
NLGN2 | 0.00004209366361 | 0.00936668395 | 0.4034811495
LEP | 0.00005452940549 | 0.00936668395 | 0.3577475602
SYNGAP1 | 0.00005123040968 | 0.00936668395 | 0.343495285
AHCYL2 | 0.00002135782271 | 0.00936668395 | 0.3178829667
NLGN1 | 0.00006078993018 | 0.00936668395 | 0.3038524214
DLGAP4 | 0.00002541551244 | 0.00936668395 | 0.2603197173
HDAC1 | 0.00007201372681 | 0.00936668395 | 0.2325809437
AMPD2 | 0.00006720473825 | 0.00936668395 | 0.2268121061
AHCYL1 | 0.00005096416925 | 0.00936668395 | 0.2225109289
SHARPIN | 0.00005017326218 | 0.00936668395 | 0.2209758038
NME2 | 0.00003378467487 | 0.00936668395 | 0.2115389973
NME1-NME2 | 0.00006948111585 | 0.00936668395 | 0.2054587747
CACNA2D3 | 0.00005109712579 | 0.00936668395 | 0.1483053899
NME3 | 0.00006668949289 | 0.00936668395 | 0.1456878904
ZC3H13 | 0.00006720473825 | 0.00936668395 | 0.09569433479
CAMK2A | 0.00007675381042 | 0.009457784822 | 0.240018082
PIK3CA | 0.0000830396655 | 0.009720725517 | 0.3056303453
MAX | 0.00009539974472 | 0.01063582059 | 0.363732902
HLA-C | 0.0001033662833 | 0.01100016766 | 0.05538126826
ACE | 0.0001142039468 | 0.01162509102 | 0.2636674328
PRKCZ | 0.0001545910345 | 0.01204914093 | 0.4117194477
NFATC3 | 0.00014401371 | 0.01204914093 | 0.2631120614
DLGAP2 | 0.0001646883876 | 0.01204914093 | 0.2621262787
GRM1 | 0.0001371182639 | 0.01204914093 | 0.2185587328
RFK | 0.0001344477253 | 0.01204914093 | 0.1994060997
PELP1 | 0.0001534640069 | 0.01204914093 | 0.1929117364
NME1 | 0.0001587901151 | 0.01204914093 | 0.1852417535
AGO1 | 0.0001623048926 | 0.01204914093 | 0.1267421033
GDPD1 | 0.0001599537131 | 0.01204914093 | 0.1120424677
GRB2 | 0.0002021723225 | 0.01239980562 | 0.552413268
DLG2 | 0.0002136072243 | 0.01239980562 | 0.3455312025
NCBP2 | 0.0001854161799 | 0.01239980562 | 0.2924464065
CDC6 | 0.000217724158 | 0.01239980562 | 0.2719220561
COASY | 0.0002224441981 | 0.01239980562 | 0.1752380172
AK2 | 0.0001872117223 | 0.01239980562 | 0.1748823525
ENTPD8 | 0.0001805611026 | 0.01239980562 | 0.1734879474
PANK2 | 0.0002151424816 | 0.01239980562 | 0.12479525
PANK1 | 0.0002115761234 | 0.01239980562 | 0.1129610957
TOP1 | 0.0001936249297 | 0.01239980562 | 0.07420560482
HOMER2 | 0.000254026573 | 0.01292898413 | 0.2270032838
GABBR1 | 0.0002504529208 | 0.01292898413 | 0.1946995021
NAMPT | 0.0002475107416 | 0.01292898413 | 0.1810894201
NME4 | 0.0002446008164 | 0.01292898413 | 0.1620683964
GDPD3 | 0.0002644117404 | 0.01317121872 | 0.1161925477
NOTCH1 | 0.0002890727325 | 0.01327027847 | 0.3451436642
NRAS | 0.0002784390332 | 0.01327027847 | 0.3023653305
DNMT3B | 0.0002758381369 | 0.01327027847 | 0.2613961857
GNRH1 | 0.0002877231424 | 0.01327027847 | 0.2216789557
RBPJL | 0.0003043123279 | 0.0132863391 | 0.22416631
PRPF4B | 0.0003064474469 | 0.0132863391 | 0.1612924054
GALT | 0.0002993841048 | 0.0132863391 | 0.1392075018
BCL2L1 | 0.0003339523837 | 0.01370050545 | 0.1743575027
STAM2 | 0.0003394075499 | 0.01370050545 | 0.1526449203
TSC2 | 0.0003308718599 | 0.01370050545 | 0.1351486918
PSMB5 | 0.0003339523837 | 0.01370050545 | 0.09214654812
DLGAP3 | 0.000383478637 | 0.01496349385 | 0.255526225
PSMB7 | 0.0003817233905 | 0.01496349385 | 0.1038468629
IK | 0.0004069892243 | 0.01562054421 | 0.1592205242
ATP4B | 0.0004201960204 | 0.01586731086 | 0.1495895259
KRT5 | 0.0004447729991 | 0.01605659595 | 0.3562476643
DVL2 | 0.0004457832546 | 0.01605659595 | 0.2472893614
NR3C2 | 0.0004337993928 | 0.01605659595 | 0.1167869506
NODAL | 0.0004696144134 | 0.01665867928 | 0.1979832889
CDC23 | 0.0004792568214 | 0.01674698322 | 0.066298297
NEDD9 | 0.0005138908073 | 0.01694554671 | 0.3002752669
RET | 0.0005013445279 | 0.01694554671 | 0.170870038
CREB5 | 0.0005058736318 | 0.01694554671 | 0.07984582323
BNIP1 | 0.0005081523588 | 0.01694554671 | 0.07174378231
CA2 | 0.0005290926327 | 0.01720450978 | 0.09000343943
MICALL2 | 0.0005495999045 | 0.0175835427 | 0.2279392609
E2F6 | 0.0005557698915 | 0.0175835427 | 0.1783243055
PTPN11 | 0.0006335750884 | 0.01760595406 | 0.4921533247
PARD6B | 0.0005994539926 | 0.01760595406 | 0.2855943649
DLGAP1 | 0.0005836861957 | 0.01760595406 | 0.2772270668
HP | 0.0006349765183 | 0.01760595406 | 0.255350692
SMARCD3 | 0.0005954755686 | 0.01760595406 | 0.2501158964
PDYN | 0.0006391979969 | 0.01760595406 | 0.1762776835
STX10 | 0.0005915215514 | 0.01760595406 | 0.1228148241
SF3B2 | 0.0005928368537 | 0.01760595406 | 0.1120273812
NMRK2 | 0.0006238447535 | 0.01760595406 | 0.09930967091
RNF41 | 0.0006252263014 | 0.01760595406 | 0.08052012925
PSMB4 | 0.0006142525691 | 0.01760595406 | 0.07241156728
ENTPD5 | 0.0006888860687 | 0.01832769356 | 0.1475714803
AK3 | 0.0006828608689 | 0.01832769356 | 0.1425136165
PPCDC | 0.0006828608689 | 0.01832769356 | 0.1295292182
CHD8 | 0.0007010838189 | 0.01839838773 | 0.2717523155
CRKL | 0.0007651146253 | 0.01839838773 | 0.2465434884
ING3 | 0.0007292603404 | 0.01839838773 | 0.1827378544
CDR2 | 0.0007634502831 | 0.01839838773 | 0.159310581
PSMC3 | 0.000770127724 | 0.01839838773 | 0.1338064671
PSMC5 | 0.0007568262067 | 0.01839838773 | 0.1311539558
SCAF1 | 0.0007453613264 | 0.01839838773 | 0.1149503644
RAPGEF1 | 0.0007437366121 | 0.01839838773 | 0.07913227333
NT5C3B | 0.0007166110143 | 0.01839838773 | 0.05687172209
IL12A | 0.0007502551103 | 0.01839838773 | 0.04846638214
INS | 0.0007785501582 | 0.01841172533 | 0.3967527371
BUB3 | 0.0007904841128 | 0.01850700877 | 0.1242775519
HSF1 | 0.0008201711148 | 0.01882553742 | 0.09274033928
CHMP3 | 0.0008130950632 | 0.01882553742 | 0.07947528694
PSMD7 | 0.0008435614855 | 0.01899006663 | 0.1163095675
PDE4B | 0.0008399234731 | 0.01899006663 | 0.1089816052
CANT1 | 0.0008564089797 | 0.01909567427 | 0.1468378314
ADGRL2 | 0.0008807471749 | 0.01945308473 | 0.2623397449
BAIAP2 | 0.0009096190052 | 0.01946857239 | 0.2936694944
PPP2R2B | 0.0009057198106 | 0.01946857239 | 0.2153577511
ENTPD6 | 0.0009313416906 | 0.01946857239 | 0.1505787491
BHMT | 0.0008960389322 | 0.01946857239 | 0.1415637578
CREB3 | 0.0009253705382 | 0.01946857239 | 0.1162738525
PSMB3 | 0.0009253705382 | 0.01946857239 | 0.07935548515
CDC14A | 0.0009433903171 | 0.01954591724 | 0.1149938292
PANK3 | 0.0009535400255 | 0.01958290698 | 0.1051019582
SF1 | 0.0009741409195 | 0.01983202362 | 0.08197664579
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Edit: Links to GeneCards added.
 

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forestglip said:
The authors have sent me Supplementary Table 2 and allowed me to share it here. They also informed me that they've uploaded it (and I assume the other tables) to MedRxiv, but it'll take some time for it to be made visible. I attached the spreadsheet to this post which appears to have every gene they tested, but here are the top 115 that were used in the model:



Edit: Links to GeneCards added.
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Thank you! Very excited to dig into it. Several of the top markers are very interesting, DMNT3A, HDAC1, [edit: CHD8] etc. are mediators of certain epigenetic marks.
 
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Yes, a very interesting list.
Lots of things relevant to central nervous and immune regulation.
I think these are going to turn out to be of real significance. It is just a bit difficult to judge to what extent the order is telling us something and to what extent it depends more on chance technical issues relating to individual rare genes.
 
I think we were all hoping for some specific top hits, but it does seem to be a little all over the board. There's hits for calcium signaling, epigenetic markers, protein kinases, enzymes involved in cAMP and MAPK signaling, purine metabolism, a lot of random one-offs like gonadotrophin releasing hormone and transcription factors associated with MYC.

Hard to find a throughline between many of them, though it might be possible to connect some to other findings in the field.
 
Jonathan Edwards said:
It is just a bit difficult to judge to what extent the order is telling us something and to what extent it depends more on chance technical issues relating to individual rare genes.
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I was hoping that their model attention score would be higher for a select few rather than a gradient like we're seeing. I'd suggest filtering above a q-value cut off and then looking at the ranking by attention difference rather than p-value.
 
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