The ClinGen Syndromic Disorders Gene Curation Expert Panel: Assessing the Clinical Validity of 111 Gene-Disease Relationships
Bibliographic record
Abstract
Purpose: The Clinical Genome Resource (ClinGen) Gene Curation Expert Panels (GCEPs) have historically focused on specific organ systems or phenotypes; thus, the ClinGen Syndromic Disorders GCEP (SD-GCEP) was formed to address an unmet need. Methods: The SD-GCEP applied ClinGen's framework to evaluate the clinical validity of genes associated with rare syndromic disorders. 111 Gene-Disease Relationships (GDRs) associated with 100 genes spanning the clinical spectrum of syndromic disorders were curated. Results: From April 2020 through March 2024, 38 precurations were performed on genes with multiple disease relationships and were reviewed to determine if the disorders were part of a spectrum or distinct entities. 14 genes were lumped into a single disease entity and 24 were split into separate entities, of which 11 were curated by the SD-GCEP. A full review of 111 GDRs for 100 genes followed, with 78 classified as Definitive, 9 as Strong, 15 as Moderate, and 9 as Limited highlighting where further data are needed. All diseases involved two or more organ systems, while the majority (88/111 GDRs, 79.2%) had five or more organ systems affected. Conclusion: The SD-GCEP addresses a critical gap in gene curation efforts, enabling inclusion of genes for syndromic disorders in clinical testing and contributing to keeping pace with the rapid discovery of new genetic syndromes.
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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 | 0.034 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".