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Record W4401125374 · doi:10.1002/ana.27040

Genomic Analysis Identifies Risk Factors in Restless Legs Syndrome

2024· review· en· W4401125374 on OpenAlex
Fulya Akçimen, Ruth Chia, Sara Sáez-Atiénzar, Paola Ruffo, Memoona Rasheed, Jay P. Ross, Calwing Liao, Anindita Ray, Patrick A. Dion, Sonja W. Scholz, Guy A. Rouleau, Bryan J. Traynor

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAnnals of Neurology · 2024
Typereview
Languageen
FieldMedicine
TopicRestless Legs Syndrome Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Neurological Disorders and StrokeIntramural Research ProgramCanadian Institutes of Health ResearchNational Institute on AgingNational Institutes of HealthGovernment of Canada
KeywordsRestless legs syndromeGenome-wide association studyGenetic architectureGeneticsGenetic associationLocus (genetics)PopulationBiologyMedicineGenePsychiatrySingle-nucleotide polymorphismQuantitative trait locusGenotypeNeurology

Abstract

fetched live from OpenAlex

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

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.841
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0050.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.215
GPT teacher head0.463
Teacher spread0.248 · 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