Phenomic determinants of genomic variation in autism spectrum disorders
Bibliographic record
Abstract
BACKGROUND: Autism spectrum disorders (ASDs) are common, heritable neurobiologic conditions of unknown aetiology confounded by significant clinical and genetic heterogeneity. METHODS: This study evaluated a broad categorisation of phenotypic traits (or phenome) for 100 subjects with Autism Diagnostic Interview-Revised/Autism Diagnostic Observation Schedule-Generic (ADI-R/ADOS-G) confirmed idiopathic ASD undergoing 1 Mb bacterial artificial chromosome (BAC) array comparative genomic hybridisation (CGH). RESULTS AND CONCLUSIONS: Array CGH uncovered nine different pathogenic copy number variants (pCNVs) in 9/100 ASD subjects having complex phenotypes (ASD+/- intellectual disability (ID; IQ<70)) and/or physical anomalies), normal karyotype, fragile X analysis, and comprehensive evaluation by a clinical geneticist. Unique pCNVs in our cohort included del(5)(p15.2p15.31) (2.4 Mb), del(3)(p24.3) (0.1 Mb) and dup(18)(p11.3)(0.9 Mb). Five pCNVs were recurrent in our cohort or were previously described in subjects with ASD+/-ID: (dup(7)(q11.23)(1.5 Mb); del(2)(p15p16.1) (6.1 Mb and 7.9 Mb); del(14)(q11.2) (0.7 Mb) and dup(15)(q11q13) (10 Mb), including del(X)(p11.22) (470 Kb) in two autistic brothers. Male: female distribution in subjects with pCNVs was reduced to 1.25:1 from 3.2:1 in the original cohort. The authors stratified the study population according to a broad spectrum of clinical features and correlated specific phenotypes with respect to CNV load and pathogenicity. The findings indicate increased prevalence of pCNVs in subjects with microcephaly (<2nd centile; n = 2 of 4 ASD subjects with microcephaly; p = 0.04), and ID (n = 9 of 64 subjects with ASD and ID; p = 0.02). Interestingly, in the absence of ID co-morbidity with an ASD, no pCNVs were found. The relationship between parental ages at delivery and CNV load and pathogenicity was also explored.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".