Twin studies in multiple sclerosis: A meta-estimation of heritability and environmentality
Bibliographic record
Abstract
BACKGROUND: Most twin studies of multiple sclerosis (MS) are inconclusive regarding the impact of genes and environment on disease susceptibility. In particular, high uncertainty exists about whether shared environmental factors are aetiologically relevant. OBJECTIVE: To disentangle, with a reasonable degree of confidence, the relative contributions of heritability and of shared and unique environmental components of MS susceptibility. METHODS: We performed a meta-analysis of previous twin studies. After a MEDLINE search, we selected eight twin studies in France, UK, Canada, Denmark, North America, Italy, Finland and Sweden. We conducted a biometric multi-group analysis under the liability-threshold model, by taking account of the study-specific ascertainment strategies and the population-specific prevalence rates of MS. RESULTS: The meta-analytic estimates of tetrachoric correlations were 0.71 (95% confidence interval (CI): 0.67-0.74) in monozygotic pairs and 0.46 (95% CI: 0.41-0.50) in dizygotic pairs. The biometric multi-group model provided meta-analytic estimates of 0.50 (95% CI: 0.39-0.61) for heritability, 0.21 (95% CI: 0.11-0.30) for shared environmental component and 0.29 (95% CI: 0.26-0.33) for unique environmental component. CONCLUSION: Our results support the continuing efforts to identify unknown genetic factors that fill the gap of 'missing heritability'; moreover, a 'missing environmentality' deserves future investigations into the role of non-heritable components that act as both shared and individual-specific exposures.
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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 | 0.061 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".