MHC transmission
Bibliographic record
Abstract
OBJECTIVE: Major histocompatibility complex (MHC) genes dominate genetic susceptibility factors in multiple sclerosis (MS). Given the general consensus that incidence and prevalence of MS has been rising and specifically in women, we evaluated MHC-gender interactions. METHODS: In a large family-based cohort consisting of 7,093 individuals (2,127 affected individuals) from 1,055 MS families, we examined MHC transmission by family structure and gender stratified by genetic distance of affected relatives from the MS proband. RESULTS: We found that affected individuals with HLA-DRB1*15-positive genotypes have higher female-to-male ratios as compared with affected individuals with HLA-DRB1*15-negative genotypes (χ(2) = 9.97, p = 0.0015) with the exception of multiplex families with 3 or more affected across 2 generations. Transmission disequilibrium test results show that HLA-DRB1*15 transmission was more distorted in collateral families vs nuclear families (χ(2) = 8.030, p = 0.0046), exclusively in affected female-female pairs (χ(2) = 7.81, p = 0.0051), but not in mixed gender pairs (χ(2) = 1.58, p = 0.21) or matched male pairs (Fisher p = 0.21). CONCLUSIONS: These observations implicate the MHC as the site of interactions and modifications mediating the female-to-male gender ratio in MS and its progressive increase. They further suggest this occurs via gene-environment interactions and epigenetic modifications in this region. The difference between collateral and nuclear families provides some insight into the inheritance, decay, and gender specificity of putative epigenetic marks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.014 |
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".