Fine mapping of the major histocompatibility complex (MHC) in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) suggests involvement of both HLA class I and class II loci
Bibliographic record
Abstract
The etiology of myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is unknown, but involvement of the immune system is one of the proposed underlying mechanisms. Human leukocyte antigen (HLA) associations are hallmarks of immune-mediated and autoimmune diseases. We have previously performed high resolution HLA genotyping and detected associations between ME/CFS and certain HLA class I and class II alleles. However, the HLA complex harbors numerous genes of immunological importance, and there is extensive and complex linkage disequilibrium across the region. In the current study, we aimed to fine map the association signals in the HLA complex by genotyping five additional classical HLA loci and 5,342 SNPs in 427 Norwegian ME/CFS patients, diagnosed according to the Canadian Consensus Criteria, and 480 healthy Norwegian controls. SNP association analysis revealed two distinct and independent association signals (p ≤ 0.001) tagged by rs4711249 in the HLA class I region and rs9275582 in the HLA class II region. Furthermore, the primary association signal in the HLA class II region was located within the HLA-DQ gene region, most likely due to HLA-DQB1, particularly the amino acid position 57 (aspartic acid/alanine) in the peptide binding groove, or an intergenic SNP upstream of HLA-DQB1. In the HLA class I region, the putative causal locus might map outside the classical HLA genes as the association signal spans several genes (DDR1, GTF2H4, VARS2, SFTA2 and DPCR1) with expression levels influenced by the ME/CFS associated SNP genotype. Taken together, our results implicate the involvement of the MHC, and in particular the HLA-DQB1 gene, in ME/CFS. These findings should be replicated in larger cohorts, particularly to verify the putative involvement of HLA-DQB1, a gene important for antigen-presentation to T cells and known to harbor alleles providing the largest risk for well-established autoimmune diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".