Detection of pathogenic copy number variants in children with idiopathic intellectual disability using 500 K SNP array genomic hybridization
Bibliographic record
Abstract
BACKGROUND: Array genomic hybridization is being used clinically to detect pathogenic copy number variants in children with intellectual disability and other birth defects. However, there is no agreement regarding the kind of array, the distribution of probes across the genome, or the resolution that is most appropriate for clinical use. RESULTS: We performed 500 K Affymetrix GeneChip array genomic hybridization in 100 idiopathic intellectual disability trios, each comprised of a child with intellectual disability of unknown cause and both unaffected parents. We found pathogenic genomic imbalance in 16 of these 100 individuals with idiopathic intellectual disability. In comparison, we had found pathogenic genomic imbalance in 11 of 100 children with idiopathic intellectual disability in a previous cohort who had been studied by 100 K GeneChip array genomic hybridization. Among 54 intellectual disability trios selected from the previous cohort who were re-tested with 500 K GeneChip array genomic hybridization, we identified all 10 previously-detected pathogenic genomic alterations and at least one additional pathogenic copy number variant that had not been detected with 100 K GeneChip array genomic hybridization. Many benign copy number variants, including one that was de novo, were also detected with 500 K array genomic hybridization, but it was possible to distinguish the benign and pathogenic copy number variants with confidence in all but 3 (1.9%) of the 154 intellectual disability trios studied. CONCLUSION: Affymetrix GeneChip 500 K array genomic hybridization detected pathogenic genomic imbalance in 10 of 10 patients with idiopathic developmental disability in whom 100 K GeneChip array genomic hybridization had found genomic imbalance, 1 of 44 patients in whom 100 K GeneChip array genomic hybridization had found no abnormality, and 16 of 100 patients who had not previously been tested. Effective clinical interpretation of these studies requires considerable skill and experience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".