Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction
Bibliographic record
Abstract
Restless legs syndrome (RLS) affects up to 1 in 10 older adults. Their health care is impeded by delayed diagnosis and insufficient treatment options. To advance disease prediction and find novel entry points for therapy, we performed a meta-analysis of genome-wide association studies (GWAS) in 116,647 cases and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci to 164, comprising 196 independent lead SNPs, with the first sex-specific GWAS on RLS revealing largely overlapping genetic predispositions of the sexes (rg=0.96). Locus annotation prioritized druggable genes such as glutamate-receptors 1 and 4. Mendelian randomization indicated RLS as a causal risk factor of diabetes. Machine-learning approaches combining genetic and environmental information performed best in risk prediction (AUC=0.82-0.91). Our study identified targets for drug development, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up, and provided evidence that gene-environment interaction is likely highly relevant for RLS risk prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".