A Genetics-First Approach to Dissecting the Heterogeneity of Autism: Phenotypic Comparison of Autism Risk Copy Number Variants
Bibliographic record
Abstract
Abstract Objective Certain copy number variants (CNVs) greatly increase risk of autism. We conducted a genetics-first study to investigate whether heterogeneity in the clinical presentation of autism is underpinned by specific genotype-phenotype relationships. Methods This international study included 547 individuals (12.3 years (SD=4.2), 54% male) who were ascertained on the basis of having a genetic diagnosis of a rare CNV associated with high risk of autism (82 16p11.2 deletion carriers, 50 16p11.2 duplication carriers, 370 22q11.2 deletion carriers and 45 22q11.2 duplication carriers), as well as 2027 individuals (9.1 years (SD=4.9), 86% male) with autism of heterogeneous aetiology. The Autism Diagnostic Interview-Revised (ADI-R) and IQ testing were conducted. Results The four genetic variant groups differed in autism severity, autism subdomain profile as well as IQ profile. However, we found substantial variability in phenotypic outcome within individual genetic variant groups (74% to 97% of the variance depending on the trait), whereas variability between groups was low (1% to 21% depending on trait). We compared CNV carriers who met autism criteria, to individuals with heterogeneous autism, and a range of profile differences were identified. Using clinical cut-offs, we found that 54% of individuals with one of the 4 CNVs who did not meet full autism diagnostic criteria nonetheless had elevated levels of autistic traits. Conclusion Many CNV carriers do not meet full diagnostic criteria for autism, but nevertheless meet clinical cut-offs for autistic traits. Although we find profile differences between variants, there is considerable variability in clinical symptoms within the same variant.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".