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Record W4401622512 · doi:10.1177/13524585241264472

Genetics of multiple sclerosis severity: The importance of statistical power in replication studies

2024· letter· en· W4401622512 on OpenAlexaff
Adil Harroud, Stephen Sawcer, Sergio E. Baranzini

Bibliographic record

VenueMultiple Sclerosis Journal · 2024
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Neurological Disorders and Stroke
KeywordsMultiple sclerosisReplication (statistics)GeneticsMedicineMEDLINEStatistical powerBiologyPsychiatryVirologyStatisticsMathematics

Abstract

fetched live from OpenAlex

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-

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 imitation

Not 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.

metaresearch head score (Codex)0.530
metaresearch head score (Gemma)0.796
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.470
Threshold uncertainty score0.579

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5300.796
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0040.004
Science and technology studies0.0040.015
Scholarly communication0.0080.009
Open science0.0070.005
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.093
GPT teacher head0.307
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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