Primary complex motor stereotypies are associated with de novo damaging DNA coding mutations that identify KDM5B as a risk gene
Bibliographic record
Abstract
Motor stereotypies are common in children with autism spectrum disorder (ASD), intellectual disability, or sensory deprivation, as well as in typically developing children ("primary" stereotypies, pCMS). The precise pathophysiological mechanism for motor stereotypies is unknown, although genetic etiologies have been suggested. In this study, we perform whole-exome DNA sequencing in 129 parent-child trios with pCMS and 853 control trios (118 cases and 750 controls after quality control). We report an increased rate of de novo predicted-damaging DNA coding variants in pCMS versus controls, identifying KDM5B as a high-confidence risk gene and estimating 184 genes conferring risk. Genes harboring de novo damaging variants in pCMS probands show significant overlap with those in Tourette syndrome, ASD, and those in ASD probands with high versus low stereotypy scores. An exploratory analysis of these pCMS gene expression patterns finds clustering within the cortex and striatum during early mid-fetal development. Exploratory gene ontology and network analyses highlight functional convergence in calcium ion transport, demethylation, cell signaling, cell cycle and development. Continued sequencing of pCMS trios will identify additional risk genes and provide greater insights into biological mechanisms of stereotypies across diagnostic boundaries.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".