A novel classification system to predict the pathogenic effects of CHD7 missense variants in CHARGE syndrome
Bibliographic record
Abstract
CHARGE syndrome is characterized by the variable occurrence of multisensory impairment, congenital anomalies, and developmental delay, and is caused by heterozygous mutations in the CHD7 gene. Correct interpretation of CHD7 variants is essential for genetic counseling. This is particularly difficult for missense variants because most variants in the CHD7 gene are private and a functional assay is not yet available. We have therefore developed a novel classification system to predict the pathogenic effects of CHD7 missense variants that can be used in a diagnostic setting. Our classification system combines the results from two computational algorithms (PolyPhen-2 and Align-GVGD) and the prediction of a newly developed structural model of the chromo- and helicase domains of CHD7 with segregation and phenotypic data. The combination of different variables will lead to a more confident prediction of pathogenicity than was previously possible. We have used our system to classify 145 CHD7 missense variants. Our data show that pathogenic missense mutations are mainly present in the middle of the CHD7 gene, whereas benign variants are mainly clustered in the 5' and 3' regions. Finally, we show that CHD7 missense mutations are, in general, associated with a milder phenotype than truncating mutations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".