Tag: Decode ME

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

Abstract:

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 = 4×10-21), 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.

Source: Lu J, Sun W, Li S, Qu Y, Liu T, Guo S, Feng C, Yang T. Assessment of symptoms in myalgic encephalomyelitis/chronic fatigue syndrome: a comparative study of existing scales. Front Neurol. 2025 Nov 18;16:1618272. doi: 10.3389/fneur.2025.1618272. PMCID: PMC12668935. https://pmc.ncbi.nlm.nih.gov/articles/PMC12668935/ (Full text available as PDF file)

Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

Myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) is a common, poorly understood disease that has no effective treatments, and has long been underserved by scientific research and national health systems. It is a sex-biased disease towards females that is often triggered by an infection, and its hallmark symptom is post-exertional malaise. People with ME/CFS often report their symptoms being disbelieved. The biological mechanisms causing ME/CFS remain unclear.
We recruited 21,620 ME/CFS cases and performed genome-wide association studies (GWAS) for up to 15,579 cases and 259,909 population controls with European genetic ancestry. In these GWAS, we discovered eight loci that are significantly associated with ME/CFS, including three near BTN2A2, OLFM4, and RABGAP1L genes that act in the response to viral or bacterial infection. Four of the eight loci (RABGAP1L, FBXL4, OLFM4, CA10) were associated at p < 0.05 with cases ascertained using post-exertional malaise and fatigue in the UK Biobank and the Netherlands biobank Lifelines. We found no evidence of sex-bias among discovered associations, and replicated in males two genetic signals (ARFGEF2, CA10) discovered in females. The ME/CFS association near CA10 colocalises with a known association to multisite chronic pain. We found no evidence that the eight ME/CFS genetic signals share common causal genetic variants with depression or anxiety.
Our findings suggest that both immunological and neurological processes are involved in the genetic risk of ME/CFS.
Source: DecodeME collaboration. Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome. https://www.research.ed.ac.uk/en/publications/initial-findings-from-the-decodeme-genome-wide-association-study- (Full text available as PDF file)

Typing myalgic encephalomyelitis by infection at onset: A DecodeME study

Abstract:

Background: People with myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) daily experience core symptoms of post-exertional malaise, unrefreshing sleep, and cognitive impairment or brain fog. Despite numbering 0.2-0.4% of the population, no laboratory test is available for their diagnosis, no effective therapy exists for their treatment, and no scientific breakthrough regarding their pathogenesis has been made. It remains unknown, despite decades of small-scale studies, whether individuals experience different types of ME/CFS separated by onset-type, sex or age.

Methods: DecodeME is a large population-based study of ME/CFS that recruited 17,074 participants in the first 3 months following full launch. Their detailed questionnaire responses provided an unparalleled opportunity to investigate illness severity, onset, course and duration.

Results: The well-established sex-bias among ME/CFS patients is evident in the initial DecodeME cohort: 83.5% of participants were females. What was not known previously was that females’ comorbidities and symptoms tend to be more numerous than males’. Moreover, being female, being older and being over 10 years from ME/CFS onset are significantly associated with greater severity.  Five different ME/CFS onset types were examined in the self-reported data: those with ME/CFS onset (i) after glandular fever (infectious mononucleosis); (ii) after COVID-19 infection; (iii) after other infections; (iv) without an identified infectious onset; and, (v) where the occurrence of an infection at or preceding onset is not known.

Conclusions: This revealed that people with a ME/CFS diagnosis are not a homogeneous group, as clear differences exist in symptomatology and comorbidity.

Source: Bretherick AD, McGrath SJ, Devereux-Cooke A et al. Typing myalgic encephalomyelitis by infection at onset: A DecodeME study [version 1; peer review: awaiting peer review]. NIHR Open Res 2023, 3:20 https://doi.org/10.3310/nihropenres.13421.1 (Full text)

DecodeME: community recruitment for a large genetics study of myalgic encephalomyelitis / chronic fatigue syndrome

Abstract:

Background: Myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) is a common, long-term condition characterised by post-exertional malaise, often with fatigue that is not significantly relieved by rest. ME/CFS has no confirmed diagnostic test or effective treatment and we lack knowledge of its causes. Identification of genes and cellular processes whose disruption adds to ME/CFS risk is a necessary first step towards development of effective therapy.

Methods: Here we describe DecodeME, an ongoing study co-produced by people with lived experience of ME/CFS and scientists. Together we designed the study and obtained funding and are now recruiting up to 25,000 people in the UK with a clinical diagnosis of ME/CFS. Those eligible for the study are at least 16 years old, pass international study criteria, and lack any alternative diagnoses that can result in chronic fatigue. These will include 5,000 people whose ME/CFS diagnosis was a consequence of SARS-CoV-2 infection. Questionnaires are completed online or on paper. Participants’ saliva DNA samples are acquired by post, which improves participation by more severely-affected individuals. Digital marketing and social media approaches resulted in 29,000 people with ME/CFS in the UK pre-registering their interest in participating. We will perform a genome-wide association study, comparing participants’ genotypes with those from UK Biobank as controls. This should generate hypotheses regarding the genes, mechanisms and cell types contributing to ME/CFS disease aetiology.

Discussion: The DecodeME study has been reviewed and given a favourable opinion by the North West – Liverpool Central Research Ethics Committee (21/NW/0169). Relevant documents will be available online ( www.decodeme.org.uk ). Genetic data will be disseminated as associated variants and genomic intervals, and as summary statistics. Results will be reported on the DecodeME website and via open access publications.

Source: Devereux-Cooke A, Leary S, McGrath SJ, Northwood E, Redshaw A, Shepherd C, Stacey P, Tripp C, Wilson J, Mar M, Boobyer D, Bromiley S, Chowdhury S, Dransfield C, Almas M, Almelid Ø, Buchanan D, Garcia D, Ireland J, Kerr SM, Lewis I, McDowall E, Migdal M, Murray P, Perry D, Ponting CP, Vitart V, Wolfe JC. DecodeME: community recruitment for a large genetics study of myalgic encephalomyelitis / chronic fatigue syndrome. BMC Neurol. 2022 Jul 19;22(1):269. doi: 10.1186/s12883-022-02763-6. PMID: 35854226. https://bmcneurol.biomedcentral.com/articles/10.1186/s12883-022-02763-6 (Full text)

 

Meet The Scientist: A conversation with Professor Chris Ponting

Professor Chris Ponting is Chair of Medical Bioinformatics at Edinburgh University and a Principal Investigator at the MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine. His research group has made substantial contributions to protein science, evolutionary biology, genetics and genomics. He has served on the editorial boards of numerous medical journals including Genome Research, Genome Biology, Human Molecular Genetics, Annual Review of Genomics and Human Genetics and Trends in Genetics. He is a Fellow of the Academy of Medical Sciences and is Principal Investigator for the Decode ME study over the next 4 years.

Professor Ponting took time out from his busy schedule to talk about the Decode ME study that seeks to understand the causes of ME. In turn, this should help with the discovery of effective treatments for ME which are so desperately needed.

The study will be the largest ever biomedical study of ME as it needs 20,000 participants. If you would like to register an interest in participating in the study then please use the link below.

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Read the rest of this interview HERE. 

Source: ME Association. July 29, 2020