Pathogenic copy number variants (pCNVs) in individuals diagnosed on the autism spectrum disorder (ASD): A closer look at candidate genes
Bibliographic record
Abstract
Introduction: The genetic basis for autism spectrum disorders (Asds) is well established and its heterogenetic nature provides us with substantial evidence for the many chromosomal aberrations associated with this complex disorder (5). however, little is known about the genes that occupy the different chromosomal regions and the gene networks they participate in as they relate to phenotypes associated with Asds. Methods: here, the author reports pathogenic copy number variants (pcnVs) validated through array-comparative genomic hybridization (cgh) and the candidate genes found on these affected regions that may be implicated in the observed clinical phenotypes in 9 patients diagnosed with an Asd. formal clinical assessments, which include a full physical examination, a medical history report, and a family history, were administered by a clinical geneticist unaware of the array-cgh results. results: The author’s findings suggest a number of genes involved in neurodevelopment as well as craniofacial and systemic features that may account for the observed phenotypes in the nine affected patients. discussion: Among the candidate genes found, the CYFIP1 gene, which is involved in maturation and maintenance of dendrites, the gamma acid receptor family (GABA) which exhibit linkage disequilibrium with autistic disorders, and the PHF8 and WNK3 genes, which have been shown to be associated with X-linked mental retardation (XlMr), present the most interesting findings as they may account for most of the neurodevelopmental pathogenesis observed in the affected patients. future studies need to be conducted in order to precisely determine the networks these genes participate in and how they are regulated to gain a deeper understanding in the roles they play in the clinical presentations of affected individuals with Asds.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".