Genetic architecture of Multiple Sclerosis patients in the French national OFSEP-HD cohort
Bibliographic record
Abstract
Abstract Multiple Sclerosis (MS) is a central nervous system (CNS) autoimmune inflammatory disease targeting the myelin sheath and affecting 2.8 million patients worldwide, mostly in economically advanced countries. The OFSEP-HD (French Multiple Sclerosis Registry - High Definition) multi-centric cohort comprises 2,667 genetic samples of patients with MS including 5 years of clinical, biological and imaging follow up. Here we described the genetic background of the cohort using data generated from the Affymetrix Precision Medicine Research Array (PMRA) genotyping chips to collect 888,799 genomic variants, and up to 8.5 million variants after imputation. Our analysis focused on genetic ancestry, admixture analysis and Human Leukocyte Antigen (HLA) including haplotypes inference. Principal Components Analysis (PCA) clustering identified seven ancestral clusters with 2177 patients (85.6 %) from clearly defined European ancestry. We observed 232 MS patients from North-African genetic ancestry while 120 of those patients (51.7%) did not self-report North-African origins, highlighting once again the limitations of self-assessed population descriptors. To promote data sharing we implemented the generation of a realistic and anonymous synthetic dataset using an adaptation of a known synthetic data generation methodology. This work unveils the genetic landscape and heterogeneous profiles of the OFSEP-HD cohort and proposes an open synthetic genetic dataset for further analyses.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".