Simon M
Senior Member (Voting Rights)
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.
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.
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.
....
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.
...
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.
...
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.
...
Full blog
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.
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.
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.
....
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.
...
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.
...
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.
...
Full blog
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