Uncovering the complexity of structural variants in four individuals with autism spectrum disorder
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is an increasingly recognized childhood developmental disorder. Despite extensive study, causal variants and molecular diagnosis remain elusive. There is both heterogeneity of the phenotype, as well as the genetic landscape associated with phenotype, which includes both inherited and de novo mutations. Currently, diagnosis is complex and behaviourally based, oftentimes occurring years after the ideal 1-2 years of age. Structural variants (SVs) are large and sometimes complex genomic variants that are likely underrepresented contributors to ASD due to the limitations of short-read DNA sequencing, such as alignment in repetitive regions and regions with GC bias. Here, we performed long-read sequencing (LRS) on four individuals with autism spectrum disorder to delineate SV complexity and determine precise breakpoints for SVs, which was not possible with short-read whole-genome sequencing (SRS). We use LRS to interrogate the methylation pattern associated with the SVs and phase the SV haplotypes to further clarify their contribution to disorder. LRS allows insight into the genome and methylome that allow us to uncover variant complexity and contribution that was previously unseen with SRS. Ultimately, this furthers precision diagnosis and contributes to individualized treatment for affected individuals and their families within the clinic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".