Genomic architecture of Autism Spectrum Disorder from comprehensive whole-genome sequence annotation
Bibliographic record
Abstract
Abstract Fully understanding the genetic factors involved in Autism Spectrum Disorder (ASD) requires whole-genome sequencing (WGS), which theoretically allows the detection of all types of genetic variants. With the aim of generating an unprecedented resource for resolving the genomic architecture underlying ASD, we analyzed genome sequences and phenotypic data from 5,100 individuals with ASD and 6,212 additional parents and siblings (total n=11,312) in the Autism Speaks MSSNG Project, as well as additional individuals from other WGS cohorts. WGS data and autism phenotyping were based on high-quality short-read sequencing (>30x coverage) and clinically accepted diagnostic measures for ASD, respectively. For initial discovery of ASD-associated genes, we used exonic sequence-level variants from MSSNG as well as whole-exome sequencing-based ASD data from SPARK and the Autism Sequencing Consortium (>18,000 trios plus additional cases and controls), identifying 135 ASD-associated protein-coding genes with false discovery rate <10%. Combined with ASD-associated genes curated from the literature, this list was used to guide the interpretation of all other variant types in WGS data from MSSNG and the Simons Simplex Collection (SSC; n=9,205). We identified ASD-associated rare variants in 789/5,100 individuals with ASD from MSSNG (15%) and 421/2,419 from SSC (17%). Considering the genomic architecture, 57% of ASD-associated rare variants were nuclear sequence-level variants, 41% were nuclear structural variants (SVs) (mainly copy number variants, but also including inversions, large insertions, uniparental isodisomies, and tandem repeat expansions), and 2% were mitochondrial variants. Several of the ASD-associated SVs would have been difficult to detect without WGS, including an inversion disrupting SCN2A and a nuclear mitochondrial insertion impacting SYNGAP1 . Polygenic risk scores did not differ between children with ASD in multiplex families versus simplex, and rare, damaging recessive events were significantly depleted in multiplex families, collectively suggesting that rare, dominant variation plays a predominant role in multiplex ASD. Our study provides a guidebook for exploring genotype-phenotype correlations in the 15-20% of ASD families who carry ASD-associated rare variants, as well as an entry point to the larger and more diverse studies that will be required to dissect the etiology in the >80% of the ASD population that remains idiopathic. All data resulting from this study are available to the medical genomics research community in an open but protected manner.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".