Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria
Genomic Health (United States) · Icahn School of Medicine at Mount Sinai · +11 more institutions
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Abstract
Methods
Using our local posterior probability-based calibration and our established data set of ClinVar pathogenic and benign variants, we determined the strength of evidence provided by 3 new tools (AlphaMissense, ESM1b, and VARITY) and calibrated scores meeting each evidence strength.
Results
All 3 tools reached the Strong level of evidence for variant pathogenicity and Moderate for benignity, although sometimes for few variants. Compared with previously recommended tools, these yielded at best only modest improvements in the trade-offs between evidence strength and false-positive predictions.
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42
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Authors
31Topics & keywords
Topics
Keywords
- Calibration
- Univariate
- Computational biology
- Medicine
- Psychology
- Computer science
- Biology
- Machine learning
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Funding
- ISIcahn School of Medicine at Mount SinaiAward: UL1TR004419
- NINational Institutes of Health
- NHNational Human Genome Research InstituteAwards: U01HG012022, R01HG013350, U01HG011755, U24HG009650, U24HG009649, HG200388-10, U24HG006834, HG200387-10
- NCNational Cancer Institute
- UNU.S. National Library of MedicineAward: R00LM012992
- NCNational Center for Advancing Translational SciencesAwards: S10OD026880, U24HG007346, S10OD030463