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Converting bioinformatic predictions to ACMG/AMP
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Classroom Contents
Approaches to Classification of Variants in TP53 and Other Hereditary Cancer Genes - Ambry Genetics
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- 1 Intro
- 2 Reminders
- 3 Logistics
- 4 Background - my variant classification activities
- 5 Overview
- 6 ACMG weighted qualitative classification system
- 7 Quantitative evidence: multifactorial likelihood analysis
- 8 Clinically calibrated bioinformatic information? Assess bioinformatic features of proven pathogenic and non-pathogenic variants in large datasets -Determine the proportion of pathogenic variants in a…
- 9 Segregation data
- 10 Other components of the model?
- 11 Alignment to ACMG codes?
- 12 Recent examples of calibration
- 13 BRCA1/2 functional assay calibration
- 14 Estimating Functional Assay LRs
- 15 BRCA1/2 splicing assay calibration
- 16 Strength of evidence for splicing data
- 17 Population allele frequency as a predictor
- 18 Strength of evidence for population frequency
- 19 TP53 ACMG code strengths - starting from scratch
- 20 Converting bioinformatic predictions to ACMG/AMP
- 21 Comparing tool performance
- 22 Comparing performance for tools in combination
- 23 Specifying PM5 ACMGIAMP rule for TP53
- 24 Using somatic data - somatic to germline ratio
- 25 Using somatic data - Second hit
- 26 Conclusions
- 27 Acknowledgements for work presented here
