Copy number variation in Han Chinese individuals with autism spectrum disorder
Bibliographic record
Abstract
BACKGROUND: Autism spectrum disorders (ASDs) are a group of neurodevelopmental conditions with a demonstrated genetic etiology. Rare (<1% frequency) copy number variations (CNVs) account for a proportion of the genetic events involved, but the contribution of these events in non-European ASD populations has not been well studied. Here, we report on rare CNVs detected in a cohort of individuals with ASD of Han Chinese background. METHODS: DNA samples were obtained from 104 ASD probands and their parents who were recruited from Harbin, China. Samples were genotyped on the Affymetrix CytoScan HD platform. Rare CNVs were identified by comparing data with 873 technology-matched controls from Ontario and 1,235 additional population controls of Han Chinese ethnicity. RESULTS: Of the probands, 8.6% had at least 1 de novo CNV (overlapping the GIGYF2, SPRY1, 16p13.3, 16p11.2, 17p13.3-17p13.2, DMD, and NAP1L6 genes/loci). Rare inherited CNVs affected other plausible neurodevelopmental candidate genes including GRID2, LINGO2, and SLC39A12. A 24-kb duplication was also identified at YWHAE, a gene previously implicated in ASD and other developmental disorders. This duplication is observed at a similar frequency in cases and in population controls and is likely a benign Asian-specific copy number polymorphism. CONCLUSIONS: Our findings help define genomic features relevant to ASD in the Han Chinese and emphasize the importance of using ancestry-matched controls in medical genetic interpretations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".