Genetics of multiple sclerosis severity: The importance of statistical power in replication studies
Bibliographic record
Abstract
In a recent study, Campagna et al. 1 revisit the association between MS severity and rs10191329, the first genome-wide significant marker for disease severity 2 .Based on their analysis of 1813 patients 3 , they report an inability to replicate our finding.In this response, we first emphasize, as acknowledged by the authors, that our study included a successful independent replication with 9,805 patients from nine centers 2 .There, as expected, the effect size was modest (β=0.044),smaller than in the discovery (β=0.088)due to the winner's curse, underscoring the challenges of replication.Indeed, Extended Data Figure 3 from our publication 2 illustrates that limited power could misleadingly suggest non-replication if centers were evaluated individually.Nevertheless, all centers were directionally concordant and collectively demonstrated a statistically significant effect.While the utility of genetic evidence in translation is firmly established irrespective of effect size 4 , it was never our intention to identify single variants that directly predict clinical outcomes, just as our discoveries of MS genetic risk factors were not expected to provide immediate diagnostic application.Second, we were intrigued by Campagna et al.'s power estimates, which seemed improbably confident (up to 84.8%) given the modest effect and their small sample size.We recalculated the power based on parameters from our replication cohort: MAF=0.17;β=0.044 for ARMSS and 0.047 for MSSS with unit variance; highest vs. lowest severity quintile OR=1.08 and 1.14, respectively.We estimated their power to detect associations with rs10191329 for binned ARMSS and MSSS at 16.7% and 38.1%, and for median ARMSS and MSSS at 16.1% and 18.8%, respectively (α=0.05).This is further evidenced by considerably wide confidence intervals (e.g., binned ARMSS 0.78-1.38).Notably, all their point estimates are directionally concordant with ours and not statistically different; random-
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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 | 0.530 | 0.796 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".