Multiple sclerosis susceptibility and the X chromosome
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic autoimmune complex trait with strong evidence for a genetic component. A female gender bias is clear but unexplained and a maternal parent-of-origin effect has been described. X-linked transmission of susceptibility has been previously proposed, based on pedigree, association and linkage studies. We genotyped 726 relative pairs including 552 affected sib-pairs for 22 X-chromosome microsatellite markers and a novel dataset of 195 aunt-uncle/niece-nephew (AUNN) affected pairs for 18 markers. Parent-of-origin effects were explored by dividing AUNN families into likely maternal and paternal trait transmission. For the sib-pair dataset we were able to establish exclusion at a lambda s = 1.9 for all markers using an exclusion threshold of LOD < or = -2. Similarly for the AUNN dataset, we established exclusion at lambdaAV = 1.9. For the combined dataset we estimate exclusion of lambda = 1.6. We did not identify significant linkage in either the sib-pairs or the AUNN dataset nor when datasets were stratified for the presence/absence of the HLA-DRB1*15 allele or for paternal or maternal transmission. This comprehensive scrutiny of the X-chromosome suggests that it is unlikely to harbour an independent susceptibility locus or one which interacts with the HLA. Complex interactions including epigenetic ones, and masking by balanced polymorphisms are mechanisms not excluded by the approach taken.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".