Sex differences in repetitive stereotyped behaviors in autism: Implications for genetic liability
Bibliographic record
Abstract
The implications of the well known sex differences in the prevalence of autism spectrum disorder (ASD) are not well understood. The aim of this paper was to investigate whether these differences might be associated with differences in genetic liability. Individuals with ASD (970 families, 2,028 individuals) were recruited as part of the Autism Genome Project (AGP). The families were differentiated into families containing a female (either female-female or male-female) and those with only males. If the sex with the lower prevalence is associated with a greater genetic liability necessary to cross sex-specific thresholds, the males from female containing families should be more severely affected than males from male only families. Affected subjects from the different types of families with ASD were sampled and compared on the social reciprocity and repetitive behavior scores from the Autism Diagnostic Interview-Revised (ADI-R). In general, females had lower repetitive behavior scores than males. More importantly, males from female containing families had higher repetitive behavior scores than males from male-male families. No such differences were apparent on the social reciprocity scores. These results support the hypothesis of a multiple threshold model of genetic liability of ASD with females having a higher liability for affectation status, at least on the repetitive behavior dimension of the disorder. These data also support the dissociation of the different phenotypic dimensions of ASD in terms of its genetic architecture. The implications of these results for linkage and association studies are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".