Genomic Analysis Identifies Risk Factors in Restless Legs Syndrome
Bibliographic record
Abstract
Objective Restless legs syndrome (RLS) is a neurological condition that causes uncomfortable sensations in the legs and an irresistible urge to move them, typically during periods of rest. The genetic basis and pathophysiology of RLS are incompletely understood. We sought to identify additional novel genetic risk factors associated with RLS susceptibility. Methods We performed a whole‐genome sequencing and genome‐wide association meta‐analysis of RLS cases ( n = 9,851) and controls ( n = 38,957) in 3 population‐based biobanks (All of Us, Canadian Longitudinal Study on Aging, and CARTaGENE). Results Genome‐wide association analysis identified 9 independent risk loci, of which 8 had been previously reported, and 1 was a novel risk locus ( LMX1B , rs35196838, OR 1.14, 95% CI 1.09–1.19, p value = 2.2 × 10 −9 ). Furthermore, a transcriptome‐wide association study also identified GLO1 and a previously unreported gene, ELFN1 . A genetic correlation analysis revealed significant common variant overlaps between RLS and neuroticism ( r g = 0.40, se = 0.08, p value = 5.4 × 10 −7 ), depression ( r g = 0.35, se = 0.06, p value = 2.17 × 10 −8 ), and intelligence ( r g = −0.20, se = 0.06, p value = 4.0 × 10 −4 ). Interpretation Our study expands the understanding of the genetic architecture of RLS, and highlights the contributions of common variants to this prevalent neurological disorder. ANN NEUROL 2024;96:994–1005
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".