Evaluation of exome filtering techniques for the analysis of clinically relevant genes
Bibliographic record
Abstract
A significant challenge facing clinical translation of exome sequencing is meaningful and efficient variant interpretation. Each exome contains ∼500 rare coding variants; laboratories must systematically and efficiently identify which variant(s) contribute to the patient's phenotype. In silico filtering is an approach that reduces analysis time while decreasing the chances of incidental findings. We retrospectively assessed 55 solved exomes using available datasets as in silico filters: Online Mendelian Inheritance in Man (OMIM), Orphanet, Human Phenotype Ontology (HPO), and Radboudumc University Medical Center curated panels. We found that personalized panels produced using HPO terms for each patient had the highest success rate (100%), while producing considerably less variants to assess. HPO panels also captured multiple diagnoses in the same individual. We conclude that custom HPO-derived panels are an efficient and effective way to identify clinically relevant exome variants.
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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".