Genetic Risk Factors for ME/CFS Identified using Combinatorial Analysis, 2022, Das et al

PrecisionLife's Combinatorial Analytics Uncovers Chronic Fatigue Syndrome Susceptibility Genes
NEW YORK – A proprietary hypothesis-free analytics method from PrecisionLife has uncovered a set of genes potentially related to risk of developing myalgic encephalomyelitis, also known as chronic fatigue syndrome. The UK firm hopes the signatures can now be used to repurpose existing medicines and to develop animal models of ME/CFS.

Steve Gardner, CEO of PrecisionLife, said that the firm's combinatorial analytics is a novel mathematics-based method that analyzes the effects of interactions between many genetic mutations.

"We look for a different set of signals than you can identify using genome-wide association studies," he said in a recent interview. Where GWAS queries relatedness between one particular SNP and a patient population, the combinatorial method detects the combined interactions of many. The computations take between four hours to a week to complete but only require fairly standard computing power, he said.

The UK company has targeted so-called complex chronic disorders with its method, such as ME/CFS, Alzheimer's disease, and sepsis, in which multiple genetic variations might come together to exert a particular effect on metabolism.

For ME/CFS in particular, the genetic underpinnings have been elusive to date. There are no animal models of the illness and no laboratory tests to conclusively diagnose it.

Patients can spend years getting a diagnosis, Gardner said, and the underlying stigma is "a massive burden" to people who are already suffering with debilitating symptoms. Worldwide, there are an estimated 20 million sufferers, "with a 100 percent unmet medical need," Gardner also said.

PrecisionLife's team applied its proprietary combinatorial analytics method to a genetic dataset of 2,382 ME/CFS patients from the UK Biobank and 4,764 controls.

The team uncovered 199 SNPs mapping to 14 genes, and these could be clustered into four pathways. Interestingly, a single SNP analysis of this same population revealed no significant genes.

Regarding the pathways, "they are all highly plausible in terms of the pathophysiology of ME/CFS," Gardner said.
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https://www.genomeweb.com/genetic-r...l-analytics-uncovers-chronic-fatigue-syndrome
 
PrecisionLife Wins BioNewsRound Award 2022

" PrecisionLife is pleased to announce that it has been named as the winner of the 2022 BioNewsRound Award at the annual Genesis 2022 event. PrecisionLife received the award for its study which discovered the First Genetic Links in ME/CFS Paving the Way for New Diagnostics and Drugs.

The BioNewsRound Award, organized by life science trade association One Nucleus, celebrates companies that have demonstrated success in advancing their programs along the innovation pipeline and recognizes advances in biopharma R&D that promise great potential to improve patient outcomes from the past year."

https://precisionlife.com/news-and-events/precisionlife-wins-bionewsround-award
 
The full paper has now been peer-reviewed and published (open access).
Genetic risk factors for ME/CFS identified using combinatorial analysis

Abstract
Background

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease that lacks known pathogenesis, distinctive diagnostic criteria, and effective treatment options. Understanding the genetic (and other) risk factors associated with the disease would begin to help to alleviate some of these issues for patients.

Methods
We applied both GWAS and the PrecisionLife combinatorial analytics platform to analyze ME/CFS cohorts from UK Biobank, including the Pain Questionnaire cohort, in a case–control design with 1000 cycles of fully random permutation. Results from this study were supported by a series of replication and cohort comparison experiments, including use of disjoint Verbal Interview CFS, post-viral fatigue syndrome and fibromyalgia cohorts also derived from UK Biobank, and compared results for overlap and reproducibility.

Results
Combinatorial analysis revealed 199 SNPs mapping to 14 genes that were significantly associated with 91% of the cases in the ME/CFS population. These SNPs were found to stratify by shared cases into 15 clusters (communities) made up of 84 high-order combinations of between 3 and 5 SNPs. p-values for these communities range from 2.3 × 10–10 to 1.6 × 10–72. Many of the genes identified are linked to the key cellular mechanisms hypothesized to underpin ME/CFS, including vulnerabilities to stress and/or infection, mitochondrial dysfunction, sleep disturbance and autoimmune development. We identified 3 of the critical SNPs replicated in the post-viral fatigue syndrome cohort and 2 SNPs replicated in the fibromyalgia cohort. We also noted similarities with genes associated with multiple sclerosis and long COVID, which share some symptoms and potentially a viral infection trigger with ME/CFS.

Conclusions
This study provides the first detailed genetic insights into the pathophysiological mechanisms underpinning ME/CFS and offers new approaches for better diagnosis and treatment of patients.
 
Key section from the published paper.

There are a number of limitations with this study discussed above, and a larger, more detailed longitudinal[*] patient dataset is likely to significantly improve the results. For this reason, we aim to replicate and extend the results from this UK Biobank study with combinatorial analysis of a future DecodeME study. DecodeME is the largest current genetic ME/CFS study, with over 20,000 participants involved [120, 121], and the more detailed patient survey data collected is likely to allow deeper insights into the different subgroups and targets involved with the disease.

ADDED:
The disease signatures in this study included 3-5 SNPs, and nothing else.. PrecisionLife saythey get better results when they can include non-genetic data in disease signatures. DecodeME data on duration, onset type, symptoms and co-morbidities will give them more to work with.

 
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Folks excuse me for being slow on the uptake here/stating the obvious but this [black box i.e. not fully public/known] technique:
  • (attempts to) go beyond the standard approach i.e. of looking for single SNIPs [which appear at higher or lower frequency]; and
  • (instead) looks for relationships between 2 or more SNIPs;
[EDIT - or another way of identifying potentially relevant single SNIPs i.e. by looking at relationships to other genes?]
So it's a way of interrogating the data which may deal with a heterogenous population [multiple disease pathologies with a single outcome - fatigue] better than the single SIP approach?

Correct?
Simon has provided an example above which demonstrates/suggests that this data mining technique worked (to some degree) in long covid - correct?
Seems interesting to me i.e. if they have/can come up with another way to interrogate the data/find common disease pathologies.
 
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@FMMM1

The approach uses combinations of features (in this case between three and 5 SNPs) to create “disease signatures“ that identify a subgroup of patients.

It’s very different from traditional GWAS, which look at the difference between individual SNPs for the entire group.

You could see it as a game of join the dots.

Imagine each patient in the cohort as a dot. The analysis looks for disease signatures shared by different individuals: this approach is then aggregated to produce “Communities“ or subgroups of patients that share overlapping disease signatures.

Effectively, common disease signatures are the line that join the dots to reveal a bird, an elephant or whatever out of a jumble of individual dots.

The approach works where a group of individuals are made up of different subgroups.

This does all lead back to specific SNPs and genes. The approach defines critical SNPs as those that occurred in numerous different disease signatures. In this case, there are 25 critical SNPs, which map to 14 different genes.


 
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Simon M said:
@FMMM1

The approach uses combinations of features (in this case between three and 5 SNPs) to create “disease signatures“ that identify a subgroup of patients.

It’s very different from traditional GWAS, which look at the difference between individual SNPs for the entire group.

You could see it as a game of join the dots.

Imagine each patient in the cohort as a dot. The analysis looks for disease signatures shared by different individuals: this approach is then aggregated to produce “Communities“ or subgroups of patients that share overlapping disease signatures.

Effectively, common disease signatures are the line that join the dots to reveal a bird, an elephant or whatever out of a jumble of individual dots.

The approach works where a group of individuals are made up of different subgroups.

This does all lead back to specific SNPs and genes. The approach defines critical SNPs as those that occurred in numerous different disease signatures. In this case, there are 25 critical SNPs, which map to 14 different genes.


Click to expand...

Thanks Simon and please don't feel obliged to respond further.

I feel that this is potentially a significant benefit and would encourage me to provide a sample (I'm not affected - I have a family member who is).
For "subgroup" I'm thinking that potentially could help to identify a biomarker and even look at repurposing any drugs which have been approved for targets on that pathway.
E.g. I recall an announcement, a few years ago, that they'd found evidence that Alzheimer's was two diseases; so that could mean that the drugs, which failed trials o the mixed population, may have worked if tested on one subgroup.

Great to see attempts to better mine data i.e. to provide insight into underlying pathology.

Wondering if this software is a spin off from the City (London financial sector) - mining data to spot relationships and make money ---- OK with that if it help those with ME/CFS!
 
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