Genetic determinants of Multiple Sclerosis susceptibility in diverse ancestral backgrounds
Bibliographic record
Abstract
Abstract The genetic architecture of Multiple Sclerosis (MS) susceptibility has been extensively assessed in populations of European ancestry. Greater ancestral diversity in genetic analyses of MS susceptibility is needed to improve the utility of Multiple Sclerosis genetic risk scores, fine map causal variants underlying established associations, and thereby enhance the identification of drug targets. Here we report findings from a genetic study of Multiple Sclerosis susceptibility in an ancestrally-diverse United Kingdom-based cohort. Participants with Multiple Sclerosis were recruited via clinical sites, an online platform, and through the United Kingdom Multiple Sclerosis Register. Phenotype data were gathered using a standardised questionnaire. DNA was extracted from saliva samples obtained remotely or in person, and participants were genotyped using a commercial genotyping array. Following imputation, cases were combined with controls from the United Kingdom Biobank and subjected to stringent quality control and genetic ancestry inference. We defined two broad ancestral groups of South Asian and African ancestry. We performed within-ancestry case-control genome-wide association studies of Multiple Sclerosis susceptibility using logistic models accounting for population structure and sex. We examined both single nucleotide variants and imputed classical Human Leukocyte Antigen alleles. We curated two ancestrally-matched case-control genetic datasets (South Asian ancestry: N Case =175, N Control =6744; African ancestry: N Case =113, N Control =5177). In both ancestries, we found genetic variants within the Major Histocompatibility Complex associated with Multiple Sclerosis susceptibility (South Asian ancestry: lead variant chr6:32600515:G:A on hg38 co-ordinates, Odds Ratio=1.84, nearest gene HLA-DRB1 , P= 4.6×10 −6 ; African ancestry: lead variant chr6:29919337:A:G, Odds Ratio=2.24, nearest gene HLA-A P= 4.3×10 −5 ). European-ancestry susceptibility alleles were over-represented in cases from both ancestries, with the degree of concordance stronger for the South Asian (ρ=0.31, P =8.1×10 −6 ) than African (ρ=0.1, P =0.3) ancestry cohort. European-derived genetic risk scores performed better than chance but less well than in European ancestry cohorts, explaining 1.6% (South Asian , P =1.0×10 −4 ) and 0.5% (African , P =0.08) of the liability to MS. The genetic architecture of MS susceptibility shows strong concordance across ancestral groups suggesting shared disease mechanisms. Larger studies in diverse populations are likely to enhance our understanding of how genetic variation contributes to MS susceptibility in people of all ancestral backgrounds.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".