Oxford-based biotech company PrecisionLife announced that their combinatorial analysis of genetic data links 14 genes to ME/CFS and identifies many patient subgroups.
According to a press release, “This is the first time that replicable genetic findings have been reported in over 30 years of study into the disease, offering new approaches for better diagnosis and treatment of patients.”
The data has been submitted for peer reviewed publication and is available on the pre-publication site medRxiv. Read it here. Learn more about the study and read Solve M.E. President Oved Amitay’s insights on this major breakthrough here.
In a detailed summary of the study for The ME/CFS Research Review, Simon McGrath writes,
“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.”
What does this mean for people with ME/CFS? Solve M.E. President Oved Amitay thinks the discovery could trigger rapid progress in ME/CFS research and the development of diagnostics and treatments:
“This study provides intriguing evidence for the notion that ME/CFS is not one disease with a single cause. It suggests that several changes in different parts of our genetic makeup, might make some people more susceptible than others, particularly if exposed to a trigger such an infection. It is also a proof of concept for importance of ‘big data.’ Large datasets, such as the You+ME Registry and the DeCode ME study, can help identify sub-groups among patients and potential underlying causes. This is an important step in that direction.”