A high-resolution copy-number variation resource for clinical and population genetics
Bibliographic record
Abstract
PURPOSE: Chromosomal microarray analysis to assess copy-number variation has become a first-tier genetic diagnostic test for individuals with unexplained neurodevelopmental disorders or multiple congenital anomalies. More than 100 cytogenetic laboratories worldwide use the new ultra-high resolution Affymetrix CytoScan-HD array to genotype hundreds of thousands of samples per year. Our aim was to develop a copy-number variation resource from a new population sample that would enable more accurate interpretation of clinical genetics data on this microarray platform and others. METHODS: Genotyping of 1,000 adult volunteers who are broadly representative of the Ontario population (as obtained from the Ontario Population Genomics Platform) was performed with the CytoScan-HD microarray system, which has 2.7 million probes. Four independent algorithms were applied to detect copy-number variations. Reproducibility and validation metrics were quantified using sample replicates and quantitative-polymerase chain reaction, respectively. RESULTS: DNA from 873 individuals passed quality control and we identified 71,178 copy-number variations (81 copy-number variations/individual); 9.8% (6,984) of these copy-number variations were previously unreported. After applying three layers of filtering criteria, from our highest confidence copy-number variation data set we obtained >95% reproducibility and >90% validation rates (73% of these copy-number variations overlapped at least one gene). CONCLUSION: The genotype data and annotated copy-number variations for this largely Caucasian population will represent a valuable public resource enabling clinical genetics research and diagnostics.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".