Whole genome sequencing and variant discovery in the ASPIRE autism spectrum disorder cohort
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is a highly heterogeneous genetic disorder with strong evidence of ASD-association currently available only for a small number of genes. This makes it challenging to identify the underlying genetic cause in many cases of ASD, and there is a continuing need for further discovery efforts. We sequenced whole genomes of 119 deeply phenotyped ASD probands in order to identify likely pathogenic variants. We prioritized variants found in each subject by predicted damage, population frequency, literature evidence, and phenotype concordance. We used Sanger sequencing to determine the inheritance status of high-priority variants where possible. We report five novel de novo damaging variants as well as several likely damaging variants of unknown inheritance; these include two novel de novo variants in the well-established ASD gene SCN2A. The availability of rich phenotypic information and its concordance with the literature allowed us to increase our confidence in pathogenicity of discovered variants, especially in probands without parental DNA. Our results contribute to the documentation of potential pathogenic variants and their associated phenotypes in individuals with ASD.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".