Genome-Wide Association Study of Sleep Disturbances in Depressive Disorders
Bibliographic record
Abstract
Sleep disturbance affects about 75% of depressed individuals and is associated with poorer patient outcomes. The genetics in this field is an emerging area of research. Thus far, only core circadian genes have been examined in this context. We expanded on this by performing a genome-wide association study (GWAS) followed by a preplanned hypothesis-driven analysis with 27 genes associated with the biology of sleep. All participants were diagnosed by their referring physician, completed the Beck Depression Inventory (BDI), and the Udvalg for Kliniske Undersogelser Side Effect Rating Scale at baseline. Our phenotype consisted of replies to 3 questions from these questionnaires. From standard GWAS chip data, imputations were performed. Baseline total BDI scores (n = 364) differed significantly between those with and those without sleep problems. We were unable to find any significant GWAS hits although our top hit was for changes in sleep and an intergenic marker near SNX18 (p = 1.06 × 10–6). None of the markers in our hypothesis-driven analysis remained significant after applying Bonferroni corrections. Our top finding among these genes was for rs13019460 of Neuronal PAS Domain Protein 2 with changes in sleep (p = 0.0009). Overall, both analyses were unable to detect any significant associations in our modest sample though we did find some interesting preliminary associations worth further exploration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".