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Record W4200623961 · doi:10.1111/jsr.13537

Does the adenosine deaminase (<i>ADA</i>) gene confer risk of sleepwalking?

2021· article· en· W4200623961 on OpenAlexafffund
Simon Fournier, Yves Dauvilliers, Simon C. Warby, Marjorie Labrecque, Antonio Zadra, Soufiane Boucetta, M. El Gewely, Houda Kaddioui, Régis Lopez, Jacques Montplaisir, Eric Bareke, Martine Tétreault, Alex Désautels

Bibliographic record

VenueJournal of Sleep Research · 2021
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalHôpital du Sacré-Cœur de MontréalCanadian Sleep & Circadian NetworkCentre Hospitalier de l’Université de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean
FundersCanadian Institutes of Health Research
KeywordsSleepwalkingGeneticsBiologyAdenosine deaminaseGeneLocus (genetics)BioinformaticsNeuroscienceSleep disorderEndocrinologyAdenosine

Abstract

fetched live from OpenAlex

Sleepwalking is a common non-rapid eye movement (NREM) parasomnia and a significant cause of sleep-related injuries. While evidence suggest that the occurrence of this condition is partly determined by genetic factors, its pattern of inheritance remains unclear, and few molecular studies have been conducted. One promising candidate is the adenosine deaminase (ADA) gene. Adenosine and the ADA enzyme play an important role in the homeostatic regulation of NREM sleep. In a single sleepwalking family, genome-wide analysis identified a locus on chromosome 20, where ADA lies. In this study, we examined if variants in the ADA gene were associated with sleepwalking. In total, 251 sleepwalking patients were clinically assessed, and DNA samples were compared to those from 94 unaffected controls. Next-generation sequencing of the whole ADA gene was performed. Bio-informatic analysis enabled the identification of variants and assessed variants enrichment in our cohort compared to controls. We detected 25 different coding and non-coding variants, of which 22 were found among sleepwalkers. None were enriched in the sleepwalking population. However, many missense variants were predicted as likely pathogenic by at least two in silico prediction algorithms. This study involves the largest sleepwalking cohort in which the role of a susceptibility gene was investigated. Our results did not reveal an association between ADA gene and sleepwalking, thus ruling out the possibility of ADA as a major genetic factor for this condition. Future work is needed to identify susceptibility genes.

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.

How this classification was reachedexpand

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)0.005
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.363
Teacher spread0.289 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes2
Has abstractyes

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