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 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 1,000 cycles of fully random permutation. The results from this study were supported by a series of replication and cohort comparison experiments, including use of a disjoint Verbal Interview cohort also derived from UK Biobank, and results compared for 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 3-5 SNPs. p-values for these communities range from 2.3 x 10-10 to 1.6 x 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 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.

Decomposing the genetic background of chronic back pain

“…We cannot consider this novel locus as statistically significant in the present study, however, it may nevertheless be a target for further investigation. This locus has been reported as associated with multisite chronic pain in females [37], major depressive disorder, osteoarthritis, post-traumatic stress disorder [38,39], and chronic fatigue syndrome [40]. Moreover, this locus contained polymorphism rs2587363 that was classified as pathogenic according to the FATHMM-XF (Supplementary Table ST13) and demonstrated a pleiotropic effect on the OLFM4 gene expression (Supplementary Table ST14) in peripheral blood.…”

“…The general formula is the following: medical code ~ age + sex + batch + PC1 + … + PC10 + PRS. Finally, we filtered out the GLM results not passing the significance threshold of p-value < 8.25e-05 = 0.05/(2*(165 + 132 + 6)), where 165 and 132 are the numbers of ICD10 and OPCS4 codes, respectively, 6 is a number of pain traits and 2 corresponds to the number of40…”