Does the adenosine deaminase (<i>ADA</i>) gene confer risk of sleepwalking?
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".