Genetics of Sleepwalking: Insights from whole exome sequencing
Bibliographic record
Abstract
Abstract Sleepwalking (SW) is a sleep disorder that belongs to the non-rapid eye movement (NREM) sleep family of parasomnias. Although linkage analyses in large families suggest that some forms of SW may follow a monogenic inheritance pattern, the genetic basis of SW has not been thoroughly investigated. The objective of this study was to investigate the role of rare genetic variants in sleepwalking by performing whole-exome sequencing (WES) in two independent cohorts. WES was performed on a cohort of 254 individuals diagnosed with SW (54.7% female, mean age: 39.1 ± 10.7 years) and 124 control individuals were selected based on age and sex (52.4% female, all aged 18 years or older), from Montreal, Canada and Montpellier, France. To be included in the SW group, probands were required to have a primary complaint of SW, undergone at least one night of video-polysomnography, and to experience at least one parasomnia episode per month. By focusing on rare, potentially deleterious genetic variants, defined as having a minor allele frequency (MAF) ≤ 5% and a Combined Annotation Dependent Depletion (CADD) pathogenicity score ≥ 15, WES allowed us to detect novel contributors to the disorder that might be missed in studies focused on common variants. We first identified 99 genes significantly enriched in patients with SW compared to the control group, with 92 genes overlapping between the two clinical cohorts. By prioritizing genes expressed in the brain, we found a strong genetic overlap between the two populations, with 31 genes carrying rare variants in common, including the top 10 genes with the highest contribution to SW compared to controls: NPIPB13, SRRM2, SIRT1, CANT1, DPYSL5, ABCC10, ELF2, DPP9 , RBM28 and MCF2L2 . Results were validated using an independent control cohort from the CARTaGENE database, except for MCF2L2 . The genes NPIPB13 , SRRM2 , and SIRT1 displayed the highest contributions in the population, with values of 13.1%, 7.5%, and 5.5%, respectively. This study represents an important step toward understanding the genetic architecture of sleepwalking, particularly the role of rare coding variants, in sleepwalking and opens new avenues for future research into the disorder’s underlying biological mechanisms.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".