Interpretation and classification of FBN1 variants associated with Marfan syndrome: consensus recommendations from the Clinical Genome Resource’s FBN1 variant curation expert panel
Bibliographic record
Abstract
BACKGROUND: In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) developed standardized variant curation guidelines for Mendelian disorders. Although these guidelines have been widely adopted, they are not gene- or disease-specific. To mitigate classification discrepancies, the Clinical Genome Resource FBN1 variant curation expert panel (VCEP) was established in 2018 to develop adaptations to the ACMG/AMP criteria for FBN1 in association with Marfan syndrome. METHODS: The specific recommendations were developed through literature review, surveys, online expert panel discussions, and pilot testing of a set of 60 different variants. Consensus among experts was considered reached if at least 75% of the members agreed with a given rule specification. The final set of rules received approval from the ClinGen Sequence Variant Interpretation Working Group. RESULTS: The developed specifications introduce modifications to 14 of the 28 ACMG/AMP evidence criteria and deem 6 criteria non-applicable. Some of these specifications include refining the minor allele frequency thresholds, creating a FBN1-specific flowchart for PVS1, defining functional domains of the protein, developing a point-based system of counting probands and instances of de novo occurrences, recommending a points-based method of accounting for family segregation data, and clarifying the applicable functional assays that should be considered. To date, this VCEP has curated 120 variants which have been deposited to ClinVar with the 3-star review status. CONCLUSIONS: Establishing specific adaptations for FBN1 has provided a framework to foster greater classification concordance among clinical laboratories, ultimately improving clinical care for patients with Marfan syndrome.
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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 | 0.001 |
| 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".