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Record W2989089017 · doi:10.1182/blood-2019-124842

Clingen Coagulation Factor Deficiency Variant Curation Expert Panel: Meeting the Need for Recommendations to Curate Variants in the Coagulation Factor Genes

2019· article· en· W2989089017 on OpenAlexaff
Shruthi Mohan, Kristy Lee, Manuel Carção, Bhavya S. Doshi, Kate Downes, Geoff Kemball-Cook, Frank W.G. Leebeek, Jamie McCreery, Connie H. Miller, Amanda B. Payne, Valerie Trapp‐Stamborski, Barbara A. Konkle, Keith Gomez

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsGeneticsCoagulationGeneComputational biologyMedical geneticsBioinformaticsMedicineBiologyInternal medicine

Abstract

fetched live from OpenAlex

The genetics of blood coagulation has been an ongoing area of research; and with the advent of next generation sequencing panels, there is a significant increase in the number of variants identified in coagulation factor genes. Several published reports and online databases document the variants observed in patients with bleeding disorders; however, the clinical interpretation of these variants is not always straight-forward. To enable gene-specific variant interpretation in coagulation factor deficiency disorders, the National Institutes of Health (NIH)-funded effort, Clinical Genome Resource (ClinGen), has developed the Coagulation Factor Deficiency Variant Curation Expert Panel (CFD-VCEP). The CFD-VCEP is comprised of expert clinicians, genetic counselors, clinical laboratory diagnosticians and researchers working toward the goal of developing and implementing standardized protocols for sequence variant interpretation for coagulation factor genes. The CFD-VCEP adapts the 2015 American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines for precise and consistent variant classification to genes involved in blood coagulation deficiencies. These guidelines recommend the use of 28 criteria codes based on the evidence category and the strength of the evidence (see Figure below). The first two genes under the purview of CFD-VCEP are F8 (OMIM: 300841) and F9 (OMIM: 300746). Pathogenic variants in the F8 and F9 genes resulting in the loss of protein function cause Hemophilia A and B, respectively. Owing to the similarity between these two genes with respect to their role in the coagulation cascade as well as the resulting phenotype, specification of variant curation guidelines for both genes has been undertaken simultaneously. With the completion of guideline specification for F8 and F9, the CFD-VCEP will subsequently continue this effort for other coagulation factor genes, while also curating F8 and F9 variants reported in ClinVar and other variant databases. Modifying the ACMG/AMP guidelines involves gene- and disease-informed specifications of the recommended criteria codes. This includes identifying which codes are applicable and which are not, defining gene- and disease-specific cut-offs such as for population frequency, and making code strength adjustments when appropriate. The specified guidelines are further refined based on their performance on a set of pilot variants (n = 30) for each gene compared to existing assertions of variant classification in ClinVar and by diagnostic laboratories represented in the CFD-VCEP. F8 and F9 variants classified by the CFD-VCEP will be submitted to ClinVar at the 3-star review status, with the tag of "FDA-recognized database", and the CFD-VCEP plans to begin this process by the second quarter of 2020. The considerations by the CFD-VCEP in the guideline-specification process and results from the pilot analysis will be discussed. This effort will lead to the standardized use of evidence criteria for the evaluation of variants in F8 and F9, which will reduce the number of variants of uncertain significance and those of conflicting interpretations, making genetic testing results more informative for providers and patients. The CFD-VCEP also encourages sharing de-identified data on variants among laboratories, which enables accurate and consistent curations. Figure Disclosures Lee: UNC Hemophilia Treatment Center: Employment. Carcao:Biotest: Honoraria, Membership on an entity's Board of Directors or advisory committees; Grifols: Honoraria, Membership on an entity's Board of Directors or advisory committees; Shire/Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; CSL Behring: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novo Nordisk Inc: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Agios: Research Funding; LFB: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bioverativ/Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Honoraria, Membership on an entity's Board of Directors or advisory committees. Kemball-Cook:European Association for Haemophilia and Allied Disorders: Other: Freelance . Leebeek:CSL Behring: Research Funding; uniQure BV: Consultancy, Research Funding; Baxalta/Shire: Research Funding. Miller:Division of Blood Disorders, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention: Consultancy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.075
metaresearch head score (Gemma)0.200
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.397

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.200
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0050.003
Science and technology studies0.0030.002
Scholarly communication0.0070.005
Open science0.0060.006
Research integrity0.0170.019
Insufficient payload (model declined to judge)0.0150.016

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.

Opus teacher head0.114
GPT teacher head0.364
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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