Preprint Identification of Novel Reproducible Combinatorial Genetic Risk Factors for [ME] in [DecodeME Cohort] and Commonalities with [LC], 2025, Sardell+

[SNT Gatchaman]

Identification of Novel Reproducible Combinatorial Genetic Risk Factors for Myalgic Encephalomyelitis in the DecodeME Patient Cohort and Commonalities with Long COVID

Spoiler: Authors

Jason Sardell; Sayoni Das; Matthew Pearson; Dmitry Kolobkov; Andrzej Malinowski; Leanne Fullwood; Marianna Sanna; Helen Baxter; Kelly McLellan; Michael Natt; Daphne Lamirel; Sonya Chowdhury; Amy Rochlin; Mark Strivens; Steve Gardner

BACKGROUND
Myalgic encephalomyelitis (also known as ME/CFS or simply ME) has severely impacted the lives of tens of millions of people globally, but the disease currently has no accurate diagnostic tools or effective treatments. Identifying the biological causes of ME has proven challenging due to its wide range of symptoms and affected organs, and the lack of reproducible genetic associations across ME populations. This has prolonged misunderstanding, lack of awareness, and denial of the disease, further harming patients.

METHODS
We used the PrecisionLife combinatorial analytics platform to identify disease signatures (i.e., combinations of 1-4 SNP-genotypes) that are significantly enriched in two cohorts of ME participants from DecodeME relative to controls from UK Biobank (UKB). We tested whether the number of these signatures possessed by an individual is significantly associated with increased prevalence of ME in a third disjoint cohort of DecodeME participants. We characterized a number of drug repurposing opportunities for a set of candidate core genes whose disease signatures had the strongest association with ME and which were linked to different mechanisms. We then tested gene overlap between the ME signatures identified and previous studies in long COVID, using two independent approaches to explore these shared genetic commonalities.

RESULTS
We identified 22,411 reproducible disease signatures, comprising combinations of 7,555 unique SNPs, that are consistently associated with increased prevalence of ME in three disjoint patient cohorts. The count of reproducible signatures was significantly associated with increased prevalence of ME (p = 4x10-21), and participants with a top 10% signature count had an odds ratio of disease 1.64 times greater than participants with a bottom 10% signature count, confirming that these genetic signatures increase susceptibility for developing ME. These disease signatures map to 2,311 genes. We identified substantial overlap between the genes found by this combinatorial analysis and previous studies. We found that the 259 candidate core genes most strongly associated with ME are enriched in disease mechanisms including neurological dysregulation, inflammation, cellular stress responses and calcium signaling. We demonstrated that 76 out of 180 genes previously linked to long COVID in UKB and the US All of Us cohorts are also significantly associated with ME in the DecodeME cohort. These findings allowed identification of many existing and novel repurposing opportunities, including candidates linked to several genes with shared etiology for long COVID.

CONCLUSION
These findings provide further evidence that ME is a complex multisystemic condition where the risk of developing the disease has a very clear genetic and biological basis. They give a substantially deeper level of insight into the genetic risk factors and mechanisms involved in ME. The discovery of so many multiply reproducible genetic associations implies that ME is highly polygenic, which has important consequences for its future study and the delivery of clinical care to patients. The striking overlap in genes and mechanisms between long COVID and ME (76 / 180 long COVID genes tested) suggests the potential for development of novel or repurposed drug therapies that could be used to successfully treat either condition. However, although they share significant genetic commonalities, long COVID and ME appear to be best considered as partially overlapping but different diseases.

Web | DOI | PDF | Preprint: MedRxiv | Open Access

 

[V.R.T.]

Good to see this out!

I don't see a data set or table of genes they found, aside from the replicated DecodeME genes. BTN2A2 was found here too, and CA10, OLFM4, SUDS3, DCC, TRIM38 and quite a few others. That's good news!

Can we say replicated when its the same cohort/dataset but different methodology? Obviously its not the same as a study replicating this data in a different cohort but just wondering on use of terminology.

What do we think of the two drug targets they claim to have found? They suggest ampligen for one and a psoriasis drug for the other.

@Jonathan Edwards was this paper the reason for your cryptic statement about drug targets possibly emerging soon but not before Thanksgiving?

They say more analysis is ongoing to confirm specific things so thats interesting.

I did a search for HLA and found nothing.

Interested to see what everyone else makes of it when people have the capacity.

 

[Utsikt]

I’m glad they are publishing some data, but what’s up with this introduction? Are there nobody in the group that are able to cut through all the babble and stick to what we actually know?

Myalgic encephalomyelitis (also known as ME/CFS or simply ME) is a complex, chronic disease characterized by post-exertional malaise (PEM, sometimes referred to as post-exertional neuroimmune exhaustion PENE (Carruthers et al. 2011) ––

First of all, it’s ME/CFS, not ME or Myalgic Encephalomyelitis. And it’s not any more complex than any other disease. It’s just not very well understood.

Second, PENE is not PEM. PEM describes a temporal pattern of symptoms, PENE describes an assume pathology.

in which symptoms disproportionately worsen, or arise, following minimal physical or mental exertion relative to pre-sickness), as well as neurological components (e.g., unrefreshing sleep, pain, neurocognitive impairment, sensory disturbance), evidence of and cognitive impairment immune/gastro-intestinal and/or genitourinary impairment, and of impairments to energy metabolism/ion transport.

I’m not sure we can say that «impairment to energy metabolism/ion transport» has been established in ME/CFS.

Patients may experience a wide spectrum of other symptoms and comorbidities affecting multiple body systems, including dysautonomia, orthostatic intolerance and postural tachycardia, fibromyalgia, IBS, clinical depression, mast cell disease, and connective tissue differences.

Dysautonomia is a meaningless description. Depression is not a part of ME/CFS, and there is no evidence of a higher prevalence. Mast cell disease is unevidenced, same wih connective tissue differences.

———

I fear that they are harming their own credibility by including these claims, and by extension, the patient group as a whole.

 

[OrganicChilli]

They suggest ampligen for one and a psoriasis drug for the other.

Apremilast (Otezla) is the other drug.

Ampligen is pretty much unavailable in every country except Argentina if I'm not mistaken.

 

[mariovitali]

The gene PDE4B they mention appears also in the genes identified by Snyder et. al : https://www.s4me.info/threads/disse...ing-powered-genome-analysis-2025-zhang.43705/

 

[Turtle]

Ampligen? That was the drug prof. Kenny de Meirleir has been promoting for decades.

 

[jnmaciuch]

I did a search for HLA and found nothing

The region was excluded (6p22.1) due to high potential for creating artifacts with their special method

 

[V.R.T.]

The region was excluded (6p22.1) due to high potential for creating artifacts with their special method

That's frustrating. I would really like more clarity about HLA, hopefully decodes separate analysis will provide it.

 

[jnmaciuch]

Am I missing something or do they never state how many participants were in the test cohort? Also, did they not do anything with the test cohort? I can’t find anywhere in the text that they mention the results of validating on the test cohort besides just mentioning that they had one.

Considering that this method would be extremely prone to overfitting, test set validation is basically the most important part…

[Edit: it is there, I just missed it scrolling through]

 

[hotblack]

I fear that they are harming their own credibility by including these claims, and by extension, the patient group as a whole

Agree. It’s disappointing to see and I fear that aspect is likely down to the two charities involved (edit, the 25% ME Group use the term PENE and their materials are cited) . Language more in keeping with DecodeME would be better. Hopefully they can be persuaded on changing this.

I don't see a data set or table of genes they found

Looks like there’s more in the extended data tables in the supplementary materials. Will need someone to go through and extract and discuss what’s relevant, sorry not up to it atm.

 

[forestglip]

Also, did they not do anything with the test cohort?

Is it this:

In order to evaluate the predictive power of the double-refined disease signatures, we used the DecodeME Test dataset (cohorts D+H) to test if the count of double-refined signatures is associated with increased odds of ME.

We observed a highly significant correlation between the signature count score and ME in the Test dataset (OR = 1.23 per standard deviation increase, p = 4x10-21) when including sex and the top 10 genetic principal components as confounders in the logistic regression. The disease odds ratio for individuals with a top 10% signature count score relative to individuals with a bottom 10% signature count score was 1.64 (Figure 7), while the odds ratio for the top 5% vs. bottom 5% was 1.89.

Am I missing something or do they never state how many participants were in the test cohort?

I think in figure 1 it says the test cohort has 3,579 cases and 113,735 controls.

 

[jnmaciuch]

Is it this:





I think in figure 1 it says the test cohort has 3,579 cases and 113,735 controls.

Thank you! I thought for sure it had to be there but wasn’t seeing it trying to scroll on my phone

 

[mariovitali]

Comma separated list of the 259 candidate core genes. Nice to see ABCA1 and ABCC6 :

AAMDC, ABCA1, ABCC6, ABHD12, ACOX3, ACTL8, ACTR3C, ADAMTS2, ADPRH, ADTRP, AFF1, AFF3, AKAP2, AKAP6, ALDOB, ANGPT1, ANKS1B, ANO3, ANO4, ARHGAP8, ASB2, ASB3, ASXL3, ATCAY, ATXN1, BAG6, BCCIP, BNC2, BRF1, BTBD2, BTBD7, BTBD9, C18orf63, C1orf87, C20orf173, C3, C4orf45, C5orf47, CACNA1A, CACNA1D, CBFA2T3, CCDC148, CCDC149, CCDC171, CCDC85A, CD22, CD82, CD8B, CDH12, CDH13, CELF2, CEP19, CH25H, CHCHD6, CHL1, CKAP4, CNTN4, COL19A1, COL4A4, COLEC12, COX17, CRYBG2, CSE1L, CSMD1, CTNNA2, CYB5RL, CYFIP1, CYP7B1, DAB1, DCC, DDAH1, DDR1, DENND2A, DHX32, DISP2, DLGAP2, DMAC1, DNAH11, DNAJA4, DNAJC25, DOCK2, DPP3, DPP6, EEPD1, EFCAB5, EHMT1, EPHB1, F13A1, FAM172A, FARP1, FBXO7, FHIT, FNTB, FOCAD, FRAS1, FREM3, FRMD4A, FTO, FUT8, GABBR1, GABRB1, GALNT18, GCNT1, GINS1, GNL3, GPSM2, GRIA1, GRIK1, GRK4, GRM7, HACD1, HIST1H2BE, HIST1H2BF, HS3ST4, IGF1R, IGF2BP3, INPP4B, KANSL1, KAZN, KCNIP4, KCNJ16, KLHDC4, KSR1, LAIR1, LOXL2, LPA, LPP, LRMDA, LRRC74A, LYPD5, MAML2, MAX, MDGA2, MED13L, MED25, MICB, MMAB, MOV10, MRPL37, MSI2, MTX2, MUC16, MYT1L, NBEAL2, NCKAP5, NCOR2, NDST3, NECTIN1, NEDD9, NFIA, NOP9, NOS1AP, NTN1, NTRK3, OR10A6, OR5AC2, OR5V1, PARD3B, PARM1, PARS2, PAX5, PCSK6, PDE1C, PDIA3, PEBP4, PIGX, PKM, PLCB1, PLD5, POPDC2, PPM1N, PPP1R36, PSMB9, PTPRD, PTPRG, PUS10, RAI14, RAP1GAP2, RASGEF1B, RASGRF2, RB1CC1, RBFOX1, RERE, RERGL, RGS7, RHBDD2, RHBDL3, RNF150, RYR2, RYR3, SAMD5, SASH1, SCAMP3, SEC23IP, SEZ6L, SFMBT2, SFTA2, SGSM2, SH3PXD2A, SH3RF3, SLC17A2, SLC25A24, SLC28A1, SLC35F3, SLC39A12, SLC5A10, SLCO2A1, SMARCA2, SNX29, SPOCK1, SPOCK3, SPTLC3, SRGAP1, ST6GAL1, STAB1, STIM2, STOX2, SUB1, SULT1C3, SYTL3, TACC1, TAP1, TENM2, THSD7A, TLR3, TM7SF3, TMEM132C, TMEM260, TMEM63C, TMTC1, TOX3, TPH2, TPX2, TRIM26, TRIM31, TSKU, TTC39C, TTLL11, TUBB, UGGT1, UNC93A, UROS, USP45, USP47, UTRN, VIT, XYLT1, YWHAB, ZC3H3, ZC3H7A, ZIC4, ZMAT4, ZNF282, ZNF283, ZNF385B, ZNF423, ZPLD1, ZSCAN5C, ZZEF1

Related to ABCA1 and ABCC6 we have the following:


ABCC6

https://www.s4me.info/threads/abcc6-and-pathogenic-snps.14251/#post-246766

and ABCA1

https://www.s4me.info/threads/presentation-at-euromene-london-uk.5760/

 

[Utsikt]

The beginning of the sections about Rintatolimod/Ampligen:

Spoiler: TLR3 and Rintatolimod

Toll-like receptor 3 (TLR3) is a key component of the innate immune system that acts as a critical sensor for viral double‐stranded RNA in several cell types that are key to host antiviral defense (Vercammen, Staal and Beyaert 2008). Beyond its role in detecting exogenous viral RNA, TLR3 also senses endogenous RNA released by damaged, necrotic, or stressed cells, thereby modulating inflammatory responses (Cavassani et al. 2008). Dysregulated TLR3 signaling can lead to chronic inflammation and tissue damage, exacerbating conditions such as autoimmune diseases, chronic viral infections, and cancer (Mohammad Hosseini et al. 2015; Hsieh et al. 2025)

Rintatolimod is a synthetic double-stranded RNA molecule that acts as a selective agonist of TLR3. On binding to TLR3, rintatolimod activates the MyD88 independent TRIF signaling pathway, leading to the production of interferons and other antiviral proteins without triggering excessive systemic inflammation associated with other dsRNA molecules (Mitchell 2016). It has been investigated in several Phase II/III clinical trials with ME patients, where it has shown statistically significant improvements in primary endpoint using exercise tolerance and some secondary endpoints when compared to placebo (Strayer et al. 2012; Mitchell 2016; Strayer, Young and Mitchell 2020).

I’m beginning to understand why most journals use numbers for references and not APA!

The references for Rintatolimod are:

Strayer DR, Carter WA, Stouch BC et al. A double-blind, placebo-controlled, randomized, clinical trial of the TLR-3 agonist rintatolimod in severe cases of chronic fatigue syndrome. PLoS One 2012;7:e31334.
Thread

Mitchell WM. Efficacy of rintatolimod in the treatment of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). Expert Rev Clin Pharmacol 2016;9:755–70.
(No thread, it’s just a narrative piece)

Strayer DR, Young D, Mitchell WM. Effect of disease duration in a randomized Phase III trial of rintatolimod, an immune modulator for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. PLoS One 2020;15:e0240403.
Thread

 

[Jonathan Edwards]

Sigh.

The idea that some very clever new genetics technique should end up with a recommendation for ampligen decorates my mental forecourt with more red flags than in front of the UN.