Exonic Mosaic Mutations Contribute Risk for Autism Spectrum Disorder
Bibliographic record
Abstract
Abstract Genetic risk factors for autism spectrum disorder (ASD) have yet to be fully elucidated. Postzygotic mosaic mutations (PMMs) have been implicated in several neurodevelopmental disorders and overgrowth syndromes. We systematically evaluated PMMs by leveraging whole-exome sequencing data on a large family-based ASD cohort, the Simons Simplex Collection. We found evidence that 11% of published single nucleotide variant (SNV) de novo mutations are potentially PMMs. We then developed a robust SNV PMM calling approach that leverages complementary callers, logistic regression modeling, and additional heuristics. Using this approach, we recalled SNVs and found that 22% of de novo mutations likely occur as PMMs in children. Unexpectedly, we found a significant burden of synonymous PMMs in probands that are predicted to alter splicing. We found no evidence of missense PMM burden in the full cohort. However, we did observe increased signal for missense PMMs in families without germline mutations in probands, which strengthens in genes intolerant to mutations. We also determined that 7-11% of parental mosaics are transmitted to children. Parental mosaic mutations make up 6.8% of all mutations newly germline in children, which has important implications for recurrence risk. PMMs intersect previously implicated high confidence and other ASD candidate risk genes, further suggesting that this class of mutations contribute to ASD risk. We also identified PMMs in novel candidate risk genes involved with chromatin remodeling or neurodevelopment. We estimate that PMMs contribute risk to 4-8% of simplex ASD cases. Overall, these findings argue for future studies of PMMs in ASD and related-disorders.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".