De Novo Insertions and Deletions of Predominantly Paternal Origin Are Associated with Autism Spectrum Disorder
Bibliographic record
Abstract
Whole-exome sequencing (WES) studies have demonstrated the contribution of de novo loss-of-function single-nucleotide variants (SNVs) to autism spectrum disorder (ASD). However, challenges in the reliable detection of de novo insertions and deletions (indels) have limited inclusion of these variants in prior analyses. By applying a robust indel detection method to WES data from 787 ASD families (2,963 individuals), we demonstrate that de novo frameshift indels contribute to ASD risk (OR = 1.6; 95% CI = 1.0-2.7; p = 0.03), are more common in female probands (p = 0.02), are enriched among genes encoding FMRP targets (p = 6 × 10(-9)), and arise predominantly on the paternal chromosome (p < 0.001). On the basis of mutation rates in probands versus unaffected siblings, we conclude that de novo frameshift indels contribute to risk in approximately 3% of individuals with ASD. Finally, by observing clustering of mutations in unrelated probands, we uncover two ASD-associated genes: KMT2E (MLL5), a chromatin regulator, and RIMS1, a regulator of synaptic vesicle release.
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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 | 0.002 |
| 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".