A Genetic Multimutation Model of Autism Spectrum Disorder Fits Disparate Twin Concordance Data from the USA and Canada
Bibliographic record
Abstract
Whether autism spectrum disorder (ASD) is caused by genetics, environmental factors, or a combination of both is still being debated today. To help resolve this issue, a genetic multimutation model of ASD development was applied to a wide variety of age-of-onset data from the USA and Canada, and the model is shown to fit all the data. Included in this analysis is new, updated data from the Interactive Autism Network (IAN) of the Kennedy Krieger Institute in Baltimore, Maryland. We find that the age-of-onset distribution for males and females is identical, suggesting that ASD may be an autosomal disorder. The ASD monozygote concordance rate in twin data predicted by the genetic multimutation model is shown to be compatible with the observed rates. If ASD is caused entirely by genetics, then the ASD concordance rate of a cohort of monozygote twins should approach 100% as the youngest pair of twins in the cohort passes 10 years of age, a prediction that constitutes a critical test of the genetic hypothesis. Thus, by measuring the ASD concordance rate as a cohort of monozygote twins age, the hypothesis that this disorder is caused entirely by genetic mutations can be tested.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".