Blog: New Study Links 14 Genes to ME/CFS

New Study Links 14 Genes to ME/CFS

A study has analysed existing genetic data in a new way to link 14 genes to ME/CFS and identify many patient subgroups. If the new approach pans out, it could transform ME research and turbocharge the development of treatments.

Paper: Genetic Risk Factors for ME/CFS Identified Using Combinatorial Analysis

Authors: Sayoni Das, Krystyna Taylor, James Kozubek, Jason Sardell, Steve Gardner

The paper has been submitted to a scientific journal and is being considered for publication. For now, the submitted draft is available as a preprint.

The study is from Oxford-based tech company PrecisionLife. It aims to find better treatments for chronic illnesses that have few or no treatment options – such as ME.

PrecisionLife uses a technique called combinatorial analysis. Big DNA studies look for differences in single DNA’ letters’, called single nucleotide polymorphisms or SNPs, pronounced “snips”. But PrecisionLife looks for combinations of these differences. They call these combinations disease signatures.

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Big DNA studies look at SNPs one by one. Combinatorial analysis looks for combinations of SNPs, which should make it easier to find links to disease.,
The study looked at DNA data from nearly 2,400 people in the UK Biobank who reported in a questionnaire that a doctor had diagnosed them with ME or CFS. The analysis found 84 statistically significant disease signatures. Each was a combination of three to five SNPs, and 199 different SNPs were involved altogether.

Of the 199, the researchers focused on 25 critical SNPs that appeared in many different disease signatures. The research team used the critical SNPs to identify14 genes connected with ME.

To put this in perspective, the only previous genetic link found to ME is for an immune-system gene, a finding that, like these new ones, needs replication.

The 14 genes affect (amongst many things) energy metabolism, susceptibility to viruses and bacteria, and sleep – all of which have an obvious link to ME.

The subgroup problem
Crucially, the study looked at how the disease signatures were shared. Many disease signatures overlapped, and the researchers combined the disease signatures into 15 subgroups. The subgroups ranged in size from 5% to 30% of the biobank ME sample. 91% of patients fell into a subgroup.

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A graphic taken from the new paper showing the 15 subgroups. Each dot represents one of the 199 SNPs clustered into subgroups. Disease signatures are not shown in an obvious way.
This is consistent with the belief of most researchers that ME/CFS is a mix of many different subgroups of patients. Each subgroup could be a different subtype of disease or even a completely different disease. This makes it very hard to find out what’s going on.

It’s as if each subgroup is a different colour: red, green, blue. If they are mixed together, we get a muddy brown, and it’s hard to see the picture.

PrecisionLife’s approach treats subgroups as the solution rather than a problem. It aims to identify groups of patients who share the same disease signature (or overlapping disease signatures). The focus on combinations of SNPs, instead of single ones, and looking for subgroups generates a stronger signal.

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Is this real?
Compared with everything published to date, these are spectacular findings. They also come from analysing a very small sample by the standards of genetic research – just 2,400 patients.

Limited success with replication
These are striking results produced by a new method, so it’s natural to be check if the results can be repeated.

The authors tried to do this, using a separate UK Biobank group. This was made up of around 1,300 people who reported in an interview that they had a diagnosis of CFS (rather than being asked if they had ME or CFS).

Success was limited. Five of the 25 critical SNPs were also statistically significant in the replication group, but none of the 84 disease signatures was. The five critical SNPs identified 2 of the 14 genes from the first group.

The paper says that technical reasons meant they were likely to miss at least some of the disease signatures or critical SNPs in the second group of patients. This is something the researchers intend to address in future studies.

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Success with other illnesses
But what makes PrecisionLife’s approach so interesting are the results they report for other diseases.

PrecisionLife made the first genetic analysis of Covid, which ran on just 725 patients from the UK Biobank. They found 68 genes of interest and reported that 48 have since been associated with Covid in published papers from other groups.
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What do the 14 genes do, and can they explain ME?
Back to the study findings. T...

How these new findings could change the landscape
If the findings from this new study do pan out, we might see rapid progress in ME research and the development of treatments.
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Full blog

 

Looks interesting. I'll leave the actual analysis to people that actually know stuff .

 

Thank you Simon for writing this

 

Very interesting, thanks Simon.

 

Thanks much for the write up. Didn’t really get replication section—if the method is robust why was there such little overlap with the second group?

 

I am probably wrong as it has been a very long night but did Chris Ponting not previously find a gene that reached significance but for females only .
I think it was associated with collagen ?

 

In UK, CFS can be a dustbin diagnosis. Idiopathic chronic fatigue gets hoovered up as well as other illnesses if there is no obvious " label" for the presenting patient.
Was this the general UK Biobank or the CureME one?
That may make a difference

ETA just read full blog and was the general Biobank and samples did not seem matched re sex and age
If they can analyse some Decode NE samples as suggested things may get interesting .

Great blog post- a real gift for explaining complex ideas .

 

It's a concern.
The authors point to two issues that might be behind the lack of replication.
1. For technical reasons (that they will address in future studies), they weren't able to run a full replication and are likely to have missed true positives.
2. The cohorts were less than ideal, coming from the UK bibobank, but only those in the biobank who either
- (main cohort) answered a Pain Questionnaire that also had a question asking if a doctor had ever given them a diagnosis of ME or CFS
— (replication group) been interviewed by the nurse (I don't know the main subject of the interview), which included a question about whether or not they'd ever been told by a doctor they had chronic fatigue syndrome.

The questionnaire/interviews were done at different times and on different people. I think the question about chronic fatigue syndrome, was open to interpretation as being about "chronic fatigue", which is far more common in CFS.

The average age of people who replied to the pain questionnaire is 69. I think it was quite a bit younger for the interview cohort.


It is likely that the two groups differ in various ways, particularly when it comes to diagnosis, making it hard so it's quite possible they were not equivalent groups of patients. If DecodeME agrees access to Precision Life for an hour and analysis, they will have better diagnosed patients and a big enough sample to split in half to give to equivalent groups. Then we will know if the findings replicated not.

Good memory! The collagen one didn't really check out, but this later post found another to a mitochondrial transporter protein. However, it was based on data in a preprint and only appeared as a blog.

thank you. Though if it was truly a gift, I don't think it would feel like such a slog to get there!