25. Identification of De novo, Deleterious Mutations through Exome Sequencing of Sporadic Autism Spectrum Disorder Trios
Bibliographic record
Abstract

 Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental disorder that has a strong genetic component consisting of many genes contributing to its cause. To help understand this complex genetic etiology, we are looking for novel genes that may be involved in the reason individuals develop ASD. We are doing this by using 5 sporadic ASD cases to determine de novo mutations (mutations new to the affected child that are not previously found in the family). These sporadic cases ensure that the disorder is not likely to arise through any inherited mutations, but through a new mutation found solely in the affected child. We use a trio analysis in which the genes of the affected child are compared to those of their mother and father, so pure de novo single nucleotide polymorphisms (SNPs) can be determined. These SNPs are then filtered based on predicted deleterious effect, quality and biological relevance. Using whole-exome sequencing on these 5 sporadic trios numerous deleterious, de novo mutations have been determined. These are being reviewed for biological relevance, and will be validated using Sanger Sequencing. Of these proposed SNPs being validated a few, such as SHANK3 and DVL1, have previously been linked to ASD. Whereas others, such as C11orf31, are novel candidate genes for the disorder. Through this experiment our understanding of the genetic etiology of ASD continues to grow and evolve, leading to greater insight into this disorder and new directions for possible treatments
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".