Bibliographic record
Abstract
Background: The mechanisms underlying the severity and wide heterogeneity of multiple sclerosis (MS) remain poorly understood. A persistent challenge has been determining whether genetic variation influences these traits. Methods: In 12,584 people with MS (pwMS), we estimated the proportion of age-related MS severity score variance attributable to additive genetic variation (SNP-heritability). Then, we interrogated for enrichment in hundreds of tissues and cell types using gene expression annotations. We performed a genome-wide association study (GWAS) using 7.8 million variants and attempted replication in an independent cohort of 9,805 pwMS. A subset of 8,325 pwMS was examined longitudinally over 54,113 visits. Results: We observed a 10% SNP-heritability for MS severity. In contrast to MS susceptibility, robust tissue-level enrichment was apparent in the brain and cervical spinal cord, but not in immune cells. We identified a novel MS severity locus (p<5×10−8) and confirmed this in the replicate population. The lead variant was associated with higher hazards of 6-month confirmed disability worsening (p=0.008) and faster EDSS worsening (p=0.002). Time to walking aid (EDSS 6.0) was 3.2 years earlier in homozygous risk carriers. Conclusions: This study identifies the first genetic modifier of MS progression, establishes the genetic contributions to its heterogeneity and describes a distinct genetic architecture from susceptibility.
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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 | 0.001 | 0.001 |
| 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) | 0.010 | 0.001 |
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".