Availability of benign missense variant “truthsets” for validation of functional assays: current status and a novel systematic approach
Bibliographic record
Abstract
Abstract Multiplex assays of variant effect (MAVEs) provide promising new sources of functional evidence, potentially empowering improved classification of germline genomic variants, particularly rare missense variants, which are commonly assigned as VUS (variants of uncertain significance). However, paradoxically, quantification of clinically applicable evidence strengths for MAVEs requires construction of “truthsets” comprising missense variants already robustly classified as pathogenic and benign. In this study, we demonstrate how benign truthset size is the primary driver of applicable functional evidence towards pathogenicity (PS3). We demonstrate, when using existing ClinVar classifications as a source of benign missense truthset variants, for only 19.8% (23/116) of established cancer susceptibility genes was a PS3 evidence strength of “strong” attainable when simulating validation for a hypothetical new MAVE (applying also favourable assumption of perfect concordance). We describe a “proactive-systematic” framework for benign truthset construction, in which all possible missense variants in a gene of interest are concurrently assessed for assignation of (likely) benignity via established ACMG/AMP combination rules including population frequency, in silico evidence codes and case-control signal. We apply this framework to eight hereditary breast and ovarian cancer genes, demonstrating that proactive-systematically generated benign missense truthsets allow maximum application of PS3 at greater (or equivalent) strength – reaching “moderate” for CHEK2 and “strong” for the other seven genes – than those derived from ClinVar ≥2* classifications alone. We propose, given many genes have few existing benign-classified missense variants, application of this proactive-systematic framework to disease genes more broadly will be important for leveraging full value from MAVEs.
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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 | 0.058 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".