Copy number variant analysis improves diagnostic yield in a diverse pediatric exome sequencing cohort
Bibliographic record
Abstract
Exome sequencing is the current standard for diagnosing Mendelian disorders; however, it is generally not considered the first-line test for detecting copy number variants (CNVs). We retrospectively investigated the additional diagnostic yield by performing concurrent CNV analysis using exome data in a large and diverse pediatric cohort. Patients were referred from various sources with variable phenotypes. Human Phenotype Ontology terms were used to prioritize variants for analysis. Ancestry and CNV analyses were performed using Somalier and NxClinical, respectively. A total of 1538 patients were tested, with the majority being Admixed Americans. Diagnostic CNVs were identified in 70 patients (4.6%), ranging from exonic deletions to large, unbalanced rearrangements, aneuploidies, and mosaic findings. While no significant differences were identified in diagnostic yield, or rates of negative or uncertain diagnoses, between ancestries, our study demonstrates the feasibility and increased yield of CNV analysis of exome data, across multiple phenotypes, referral sources, and ancestries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".