The Clinical Genome Resource (ClinGen) Familial Hypercholesterolemia Variant Curation Expert Panel consensus guidelines for <i>LDLR</i> variant classification
Bibliographic record
Abstract
ABSTRACT Purpose In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published consensus standardized guidelines for variant classification in Mendelian disorders. To increase accuracy and consistency, the Clinical Genome Resource (ClinGen) Familial Hypercholesterolemia (FH) Variant Curation Expert Panel (VCEP) was tasked with optimizing the existing ACMG/AMP framework for disease-specific classification in FH. Here, we provide consensus recommendations for the most common FH-causing gene, LDLR , where >2,300 unique FH-associated variants have been identified. Methods The multidisciplinary FH VCEP met in person and through frequent emails and conference calls to develop LDLR -specific modifications of ACMG/AMP guidelines. Through iteration, pilot testing, debate and commentary, consensus among experts was reached. Results The consensus LDLR variant modifications to existing ACMG/AMP guidelines include: 1) alteration of population frequency thresholds; 2) delineation of loss-of-function variant types; 3) functional study criteria specifications; 4) co-segregation criteria specifications; and 5) specific use and thresholds for in silico prediction tools, among others. Conclusion Establishment of these guidelines as the new standard in the clinical laboratory setting will result in a more evidence-based, harmonized method for LDLR variant classification worldwide, thereby improving the care of FH patients.
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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 | 0.078 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".