Tag: SNP profiles

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)

No replication of previously reported association with genetic variants in the T cell receptor alpha (TRA) locus for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disease with a variety of symptoms such as post-exertional malaise, fatigue, and pain, but where aetiology and pathogenesis are unknown. An increasing number of studies have implicated the involvement of the immune system in ME/CFS. Furthermore, a hereditary component is suggested by the reported increased risk for disease in relatives, and genetic association studies are being performed to identify potential risk variants.

We recently reported an association with the immunologically important human leucocyte antigen (HLA) genes HLA-C and HLA-DQB1 in ME/CFS. Furthermore, a genome-wide genetic association study in 42 ME/CFS patients reported significant association signals with two variants in the T cell receptor alpha (TRA) locus (P value <5 × 10-8). As the T cell receptors interact with the HLA molecules, we aimed to replicate the previously reported findings in the TRA locus using a large Norwegian ME/CFS cohort (409 cases and 810 controls) and data from the UK biobank (2105 cases and 4786 controls).

We investigated numerous SNPs in the TRA locus, including the two previously ME/CFS-associated variants, rs11157573 and rs17255510. No associations were observed in the Norwegian cohort, and there was no significant association with the two previously reported SNPs in any of the cohorts. However, other SNPs showed signs of association (P value <0.05) in the UK Biobank cohort and meta-analyses of Norwegian and UK biobank cohorts, but none survived correction for multiple testing. Hence, our research did not identify any reliable associations with variants in the TRA locus.

Source: Ueland M, Hajdarevic R, Mella O, Strand EB, Sosa DD, Saugstad OD, Fluge Ø, Lie BA, Viken MK. No replication of previously reported association with genetic variants in the T cell receptor alpha (TRA) locus for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Transl Psychiatry. 2022 Jul 11;12(1):277. doi: 10.1038/s41398-022-02046-1. PMID: 35821115. https://www.nature.com/articles/s41398-022-02046-1 (Full text)

Autoimmunity-Related Risk Variants in PTPN22 and CTLA4 Are Associated With ME/CFS With Infectious Onset

Abstract:

Single nucleotide polymorphisms (SNP) in various genes have been described to be associated with susceptibility to autoimmune disease. In this study, myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) patients and controls were genotyped for five immune gene SNPs in tyrosine phosphatase non-receptor type 22 (PTPN22, rs2476601), cytotoxic T-lymphocyte-associated protein 4 (CTLA4, rs3087243), tumor necrosis factor (TNF, rs1800629 and rs1799724), and interferon regulatory factor 5 (IRF5, rs3807306), which are among the most important risk variants for autoimmune diseases.

Analysis of 305 ME/CFS patients and 201 healthy controls showed significant associations of the PTPN22 rs2476601 and CTLA4 rs3087243 autoimmunity-risk alleles with ME/CFS. The associations were only found in ME/CFS patients, who reported an acute onset of disease with an infection (PTPN22 rs2476601: OR 1.63, CI 1.04-2.55, p = 0.016; CTLA4 rs3087243: OR 1.53, CI 1.17-2.03, p = 0.001), but not in ME/CFS patients without infection-triggered onset (PTPN22 rs2476601: OR 1.09, CI 0.56-2.14, p = 0.398; CTLA4 rs3087243: OR 0.89, CI 0.61-1.30, p = 0.268). This finding provides evidence that autoimmunity might play a role in ME/CFS with an infection-triggered onset. Both genes play a key role in regulating B and T cell activation.

Source: Steiner S, Becker SC, Hartwig J, Sotzny F, Lorenz S, Bauer S, Löbel M, Stittrich AB, Grabowski P, Scheibenbogen C. Autoimmunity-Related Risk Variants in PTPN22 and CTLA4 Are Associated With ME/CFS With Infectious Onset. Front Immunol. 2020 Apr 9;11:578. doi: 10.3389/fimmu.2020.00578. eCollection 2020. https://www.ncbi.nlm.nih.gov/pubmed/32328064

Genome-wide association analysis identifies genetic variations in subjects with myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

Myalgic encephalomyelitis, also known as chronic fatigue syndrome or ME/CFS, is a multifactorial and debilitating disease that has an impact on over 4 million people in the United States alone. The pathogenesis of ME/CFS remains largely unknown; however, a genetic predisposition has been suggested.

In the present study, we used a DNA single-nucleotide polymorphism (SNP) chip representing over 906,600 known SNPs to analyze DNA from ME/CFS subjects and healthy controls. To the best of our knowledge, this study represents the most comprehensive genome-wide association study (GWAS) of an ME/CFS cohort conducted to date.

Here 442 SNPs were identified as candidates for association with ME/CFS (adjusted P-value<0.05). Whereas the majority of these SNPs are represented in non-coding regions of the genome, 12 SNPs were identified in the coding region of their respective gene. Among these, two candidate SNPs resulted in missense substitutions, one in a pattern recognition receptor and the other in an uncharacterized coiled-coil domain-containing protein. We also identified five SNPs that cluster in the non-coding regions of T-cell receptor loci.

Further examination of these polymorphisms may help identify contributing factors to the pathophysiology of ME/CFS, as well as categorize potential targets for medical intervention strategies.

 

Source: Schlauch KA, Khaiboullina SF, De Meirleir KL, Rawat S, Petereit J, Rizvanov AA, Blatt N, Mijatovic T, Kulick D, Palotás A, Lombardi VC. Genome-wide association analysis identifies genetic variations in subjects with myalgic encephalomyelitis/chronic fatigue syndrome. Transl Psychiatry. 2016 Feb 9;6:e730. doi: 10.1038/tp.2015.208. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4872418/ (Full article)

 

Mitochondrial DNA variants correlate with symptoms in myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

BACKGROUND: Mitochondrial dysfunction has been hypothesized to occur in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a disease characterized by fatigue, cognitive difficulties, pain, malaise, and exercise intolerance. We investigated whether haplogroup, single nucleotide polymorphisms (SNPs), or heteroplasmy of mitochondrial DNA (mtDNA) were associated with health status and/or symptoms.

METHODS: Illumina sequencing of PCR-amplified mtDNA was performed to analyze sequence and extent of heteroplasmy of mtDNAs of 193 cases and 196 age- and gender-matched controls from DNA samples collected by the Chronic Fatigue Initiative. Association testing was carried out to examine possible correlations of mitochondrial sequences with case/control status and symptom constellation and severity as reported by subjects on Short Form-36 and DePaul Symptom Questionnaires.

RESULTS: No ME/CFS subject exhibited known disease-causing mtDNA mutations. Extent of heteroplasmy was low in all subjects. Although no association between mtDNA SNPs and ME/CFS vs. healthy status was observed, haplogroups J, U and H as well as eight SNPs in ME/CFS cases were significantly associated with individual symptoms, symptom clusters, or symptom severity.

CONCLUSIONS: Analysis of mitochondrial genomes in ME/CFS cases indicates that individuals of a certain haplogroup or carrying specific SNPs are more likely to exhibit certain neurological, inflammatory, and/or gastrointestinal symptoms. No increase in susceptibility to ME/CFS of individuals carrying particular mitochondrial genomes or SNPs was observed.

 

Source: Billing-Ross P, Germain A, Ye K, Keinan A, Gu Z, Hanson MR. Mitochondrial DNA variants correlate with symptoms in myalgic encephalomyelitis/chronic fatigue syndrome. J Transl Med. 2016 Jan 20;14:19. doi: 10.1186/s12967-016-0771-6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4719218/ (Full article)

 

Pathway-focused genetic evaluation of immune and inflammation related genes with chronic fatigue syndrome

Abstract:

Recent evidence suggests immune and inflammatory alterations are important in chronic fatigue syndrome (CFS). This study was done to explore the association of functionally important genetic variants in inflammation and immune pathways with CFS. Peripheral blood DNA was isolated from 50 CFS and 121 non-fatigued (NF) control participants in a population-based study. Genotyping was performed with the Affymetrix Immune and Inflammation Chip that covers 11K single nucleotide polymorphisms (SNPs) following the manufacturer’s protocol.

Genotyping accuracy for specific genes was validated by pyrosequencing. Golden Helix SVS software was used for genetic analysis. SNP functional annotation was done using SPOT and GenomePipe programs. CFS was associated with 32 functionally important SNPs: 11 missense variants, 4 synonymous variants, 11 untranslated regulatory region (UTR) variants and 6 intronic variants. Some of these SNPs were in genes within pathways related to complement cascade (SERPINA5, CFB, CFH, MASP1 and C6), chemokines (CXCL16, CCR4, CCL27), cytokine signaling (IL18, IL17B, IL2RB), and toll-like receptor signaling (TIRAP, IRAK4).

Of particular interest is association of CFS with two missense variants in genes of complement activation, rs4151667 (L9H) in CFB and rs1061170 (Y402H) in CFH. A 5′ UTR polymorphism (rs11214105) in IL18 also associated with physical fatigue, body pain and score for CFS case defining symptoms. This study identified new associations of CFS with genetic variants in pathways including complement activation providing additional support for altered innate immune response in CFS. Additional studies are needed to validate the findings of this exploratory study.

Published by Elsevier Inc.

 

Source: Rajeevan MS, Dimulescu I, Murray J, Falkenberg VR, Unger ER. Pathway-focused genetic evaluation of immune and inflammation related genes with chronic fatigue syndrome. Hum Immunol. 2015 Aug;76(8):553-60. doi: 10.1016/j.humimm.2015.06.014. Epub 2015 Jun 24. https://www.ncbi.nlm.nih.gov/pubmed/26116897

 

Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome

Abstract:

BACKGROUND: The current practice of using only a few strongly associated genetic markers in regression models results in generally low power in prediction or accounting for heritability of complex human traits.

PURPOSE: We illustrate here a Bayesian joint estimation of single nucleotide polymorphism (SNP) effects principle to improve prediction of phenotype status from pathway-focused sets of SNPs. Chronic fatigue syndrome (CFS), a complex disease of unknown etiology with no laboratory methods for diagnosis, was chosen to demonstrate the power of this Bayesian method. For CFS, such a genetic predictive model in combination with clinical evidence might lead to an earlier diagnosis than one based solely on clinical findings.

METHODS: One of our goals is to model disease status using Bayesian statistics which perform variable selection and parameter estimation simultaneously and which can induce the sparseness and smoothness of the SNP effects. Smoothness of the SNP effects is obtained by explicit modeling of the covariance structure of the SNP effects.

RESULTS: The Bayesian model achieved perfect goodness of fit when tested within the sampled data. Tenfold cross-validation resulted in 80% accuracy, one of the best so far for CFS in comparison to previous prediction models. Model reduction aspects were investigated in a computationally feasible manner. Additionally, genetic variation estimates provided by the model identified specific genetic markers for their biological role in the disease pathophysiology.

CONCLUSIONS: This proof-of-principle study provides a powerful approach combining Bayesian methods, SNPs representing multiple pathways and rigorous case ascertainment for accurate genetic risk prediction modeling of complex diseases like CFS and other chronic diseases.

 

Source: Bhattacharjee M, Rajeevan MS, Sillanpää MJ. Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome. Hum Genomics. 2015 Jun 11;9:8. doi: 10.1186/s40246-015-0030-6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479222/ (Full article)

 

Use of single-nucleotide polymorphisms (SNPs) to distinguish gene expression subtypes of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME)

Abstract:

AIMS: We have reported gene expression changes in patients with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) and the fact that such gene expression data can be used to identify subtypes of CFS/ME with distinct clinical phenotypes. Due to the difficulties in using a comparative gene expression method as an aid to CFS/ME disease and subtype-specific diagnosis, we have attempted to develop such a method based on single-nucleotide polymorphism (SNP) analysis.

METHODS: To identify SNP allele associations with CFS/ME and CFS/ME subtypes, we tested genomic DNA of patients with CFS/ME (n=108), patients with endogenous depression (n=17) and normal blood donors (n=68) for 504 human SNP alleles located within 88 CFS-associated human genes using the SNP Genotyping GoldenGate Assay (Illumina, San Diego, California, USA). 360 ancestry informative markers (AIM) were also examined.

RESULTS: 21 SNPs were significantly associated with CFS/ME compared with depression and normal groups. 148 SNP alleles had a significant association with one or more CFS/ME subtypes. For each subtype, associated SNPs tended to be grouped together within particular genes. AIM SNPs indicated that 4 subjects were of Asian origin while the remainder were Caucasian. Hierarchical clustering of AIM data revealed the relatedness between 2 couples of patients with CFS only and confirmed the overall heterogeneity of all subjects.

CONCLUSIONS: This study provides evidence that human SNPs located within CFS/ME associated genes are associated with particular genomic subtypes of CFS/ME. Further work is required to develop this into a clinically useful subtype-specific diagnostic test.

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

 

Source: Shimosako N, Kerr JR. Use of single-nucleotide polymorphisms (SNPs) to distinguish gene expression subtypes of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). J Clin Pathol. 2014 Dec;67(12):1078-83. doi: 10.1136/jclinpath-2014-202597. Epub 2014 Sep 19. https://www.ncbi.nlm.nih.gov/pubmed/25240059

 

Supervised selection of single nucleotide polymorphisms in chronic fatigue syndrome

Abstract:

INTRODUCTION: The different ways for selecting single nucleotide polymorphisms have been related to paradoxical conclusions about their usefulness in predicting chronic fatigue syndrome even when using the same dataset.

OBJECTIVE: To evaluate the efficacy in predicting this syndrome by using polymorphisms selected by a supervised approach that is claimed to be a method that helps identifying their optimal profile.

MATERIALS AND METHODS: We eliminated those polymorphisms that did not meet the Hardy-Weinberg equilibrium. Next, the profile of polymorphisms was obtained through the supervised approach and three aspects were evaluated: comparison of prediction accuracy with the accuracy of a profile that was based on linkage disequilibrium, assessment of the efficacy in determining a higher risk stratum, and estimating the algorithm influence on accuracy.

RESULTS: A valid profile (p<0.01) was obtained with a higher accuracy than the one based on linkage disequilibrium, 72.8 vs. 62.2% (p<0.01). This profile included two known polymorphisms associated with chronic fatigue syndrome, the NR3C1_11159943 major allele and the 5HTT_7911132 minor allele. Muscular pain or sinus nasal symptoms in the stratum with the profile predicted V with a higher accuracy than those symptoms in the entire dataset, 87.1 vs. 70.4% (p<0.01) and 92.5 vs. 71.8% (p<0.01) respectively. The profile led to similar accuracies with different algorithms.

CONCLUSIONS: The supervised approach made it possible to discover a reliable profile of polymorphisms associated with this syndrome. Using this profile, accuracy for this dataset was the highest reported and it increased when the profile was combined with clinical data.

 

Source: Cifuentes RA, Barreto E. Supervised selection of single nucleotide polymorphisms in chronic fatigue syndrome. Biomedica. 2011 Oct-Dec;31(4):613-21. doi: 10.1590/S0120-41572011000400017. http://www.scielo.org.co/pdf/bio/v31n4/v31n4a17.pdf (Full article)

 

Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data

Abstract:

We start by constructing gene-gene association networks based on about 300 genes whose expression values vary between the groups of CFS patients (plus control). Connected components (modules) from these networks are further inspected for their predictive ability for symptom severity, genotypes of two single nucleotide polymorphisms (SNP) known to be associated with symptom severity, and intensity of the ten most discriminative protein features. We use two different network construction methods and choose the common genes identified in both for added validation. Our analysis identified eleven genes which may play important roles in certain aspects of CFS or related symptoms. In particular, the gene WASF3 (aka WAVE3) possibly regulates brain cytokines involved in the mechanism of fatigue through the p38 MAPK regulatory pathway.

 

Source: Pihur V, Datta S, Datta S. Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data. Bioinformation. 2011 Apr 22;6(3):120-4. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3089886/ (Full article)