Genetic Association Studies in Restless Legs Syndrome: Risk Variants & Ethnic Differences
Bibliographic record
Abstract
BACKGROUND: Genetic association studies have not produced consistent results in restless legs syndrome (RLS). OBJECTIVES: To conduct a systematic review on genetic association studies in RLS to highlight the common gene variants and ethnic differences. METHODOLOGY: We conducted Pubmed, Embase, and Cochrane search using terms "Genetic association studies" and "restless legs syndrome" for candidate gene-based studies. Out of the initial 43 studies, 18 case control studies (from 2012 to 2022) were included. Thirteen studies including 10794 Caucasian subjects (4984 RLS cases and 5810 controls) and five studies involving 2009 Asian subjects (796 RLS cases and 1213 controls) were tabulated and analyzed. In addition, three Genome-Wide Association Studies (GWAS) in Asians and Europeans/Caucasians were included for comparisons. RESULTS: In the Asian population, gene variants in BST1, SNCA Rep1, IL1B, BTBD9, and MAP2K5/SKOR1 increased the risk of RLS (odds ratio range 1.2-2.8). In Caucasian populations, examples of variants that were associated with an increased risk of RLS (odds ratio range 1.1-1.9) include those in GABRR3 TOX3, ADH1B, HMOX1, GLO1, DCDC2C, BTBD9, SKOR1, and SETBP1. Based on the meta-analysis of GWAS studies, the rs9390170 variant in UTRN gene was identified to be a novel genetic marker for RLS in Asian cohorts, whereas rs113851554 in MEIS1 gene was a strong genetic factor among the >20 identified gene variants for RLS in Caucasian populations. CONCLUSION: Our systemic review demonstrates that multiple genetic variants modulate risk of RLS in Caucasians (such as MEIS1 BTBD9, MAP2K5) and in Asians (such as BTBD9, MAP2K5, and UTRN).
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".