Decomposing the autism phenotype into familial dimensions
Bibliographic record
Abstract
The objective of this article is to decompose the level of functioning phenotype in autism to see if it can be conceptualized as two simpler, but still familial, dimensional phenotypes of language and non-verbal IQ. We assembled 80 sibpairs with either autism, Asperger syndrome or atypical autism. To see whether the familial correlation on language scores was accounted for by the familial correlation on non-verbal IQ, residual language scores were calculated for each member of the sibpair based on a multiple regression equation using their IQ score as an explanatory or independent variable and controlling for the age and gender of the affected individual. These residual scores were then used to calculate intraclass correlations between affected sibs. This process was repeated using IQ as the dependent variable and language as a covariate. Within affected individuals there was a strong relation between non-verbal IQ (as measured by the Leiter performance scale) and language (as measured by the Vineland Communication Scale). In addition, there was familial correlation between sibs on both measures. Evidence of familial aggregation on both non-verbal IQ and language remained even after partialling out the effect of the covariates by regression analysis and by generalized estimating equation. These findings suggest that non-verbal IQ and language in PDD may arise from independent genetic mechanisms. The implications of this finding for linkage analysis and for identifying genetically informative phenotypes 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".