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Ranking and classifying the VUS for family counseling: Using a model - - PowerPoint PPT Presentation
Ranking and classifying the VUS for family counseling: Using a model - - PowerPoint PPT Presentation
Ranking and classifying the VUS for family counseling: Using a model from cancer researchers Martin Tristani-Firouzi, MD Division of Pediatric Cardiology Nora Eccles Harrison CVRTI University of Utah School of Medicine Disclosure The speaker
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Variants / genome (million)
150 protein truncating variants 10,000 amino acid changing variants 500,000 variants in regulatory regions
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The dreaded result: a variant of unknown significance (VUS)
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How do we interpret the significance of VUS?
- Segregation of variant w/ disease in families
- Occurrence in multiple unrelated individuals
- Functional assays
- In silico prediction algorithms
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Most VUS are rare and “private”
- Segregation of variant w/ disease in families
- Occurrence in multiple unrelated individuals
- Functional assays
- In silico prediction algorithms
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SIFT: Sorting Tolerant from Intolerant tool
Kumar et al, Nature Protocols, 2009
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Physico-chemical properties of amino acid substitution: predicted mutation effect
Livingstone & Barton, CABIOS, 9, 745-756, 1993
polar charged hydrophobic
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Other in silico tools for pathogenicity prediction
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Meta-SVM: combining multiple prediction tools improves accuracy
Dong et al, Hum Mol Genetics, 2015
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LQTS and BrS SCN5A variants
Receiver operator curve for when >4 in silico tool are in agreement
True positive rate False positive rate
Kapplinger et al, Circ Cardio Genet, 2015
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The addition of topology to pathogenicity prediction
Whicher and MacKinnon, Science, 2016
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The addition of topology to pathogenicity prediction
Kapplinger et al, Circ Cardio Genet, 2015
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The Cancer Field approach to VUS
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Variant classification scheme
Class 5- Pathogenic, > 99% probability of pathogenicity Class 4- Likely Pathogenic, 95-99% probability of pathogenicity Class 3- Uncertain, 5-95% probability of pathogenicity Class 2- Likely Neutral, 0.1-5% probability of pathogenicity Class 1- Neutral, <0.1% probability of pathogenicity
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Bayesian multi-factorial model of pathogenicity
Posterior OR = Prior OR x OR for Pathogenicity from Data (Di) predictive value for each Di is the probability of Class 5 pathogenicity, P(Di|C5) divided by probability of Class 1 benign, P(Di|C1) D1= in silico D2= functional assay D3= family history Prior OR x P(D1|C5)/P(D1|C1) x P(D2|C5)/P(D2|C1) x P(D3|C5)/P(D3|C1)
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Variant classification scheme and clinical recommendations
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Applying the Bayesian multi-factorial model
- f pathogenicity for LQTS
Posterior OR = Prior OR x OR for Pathogenicity from Data (Di) predictive value for each Di is the probability of Class 5 pathogenicity, P(Di|C5) divided by probability of Class 1 benign, P(Di|C1) Prior OR x P(D1|C5)/P(D1|C1) x P(D2|C5)/P(D2|C1) x P(D3|C5)/P(D3|C1) D1= in silico D2= functional assay D3= family history
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Case-control comparison for LQTS variants (prior odds)
Ruklisa et al, Genome Med, 2015
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Posterior OR = Prior OR x OR for Pathogenicity from Data (Di) predictive value for each Di is the probability of Class 5 pathogenicity, P(Di|C5) divided by probability of Class 1 benign, P(Di|C1) D1= in silico D2= functional assay D3= family history Prior OR x P(D1|C5)/P(D1|C1) x P(D2|C5)/P(D2|C1) x P(D3|C5)/P(D3|C1)
Applying the Bayesian multi-factorial model
- f pathogenicity for LQTS
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Functional characterization of candidate variants
zebrafish Human iPSC-CMs
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Why zebrafish?: repolarization properties similar to human
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High throughput screening platform for phenotype-based repolarization screen
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Functional effects of putative LQT2 mutations and polymorphisms as determined by zebrafish cardiac assay
LQT-2 mutants
+/- 95% CI
polymorphisms
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Comparison of in vivo zebrafish cardiac assay with in vitro mammalian cell assay
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Posterior OR = Prior OR x OR for Pathogenicity from Data (Di) predictive value for each Di is the probability of Class 5 pathogenicity, P(Di|C5) divided by probability of Class 1 benign, P(Di|C1) D1= in silico D2= functional assay D3= family history Prior OR x P(D1|C5)/P(D1|C1) x P(D2|C5)/P(D2|C1) x P(D3|C5)/P(D3|C1)
Applying the Bayesian multi-factorial model
- f pathogenicity for LQTS
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Findmyvariant.org
The FindMyVariant team is affiliated with the University of Washington, Department of Laboratory testing.
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Steps involved in investigating the family disease
- Talking with Your Immediate
Family About Your Variant
- Talking with Living Relatives
to Find Your Ancestors
- Using Online Social
Networking Sites to Find Descendants of Your Ancestors
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Final family history and pedigree
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Bayesian multi-factorial model of pathogenicity
Posterior OR = Prior OR x OR for Pathogenicity from Data (Di) predictive value for each Di is the probability of Class 5 pathogenicity, P(Di|C5) divided by probability of Class 1 benign, P(Di|C1) D1= in silico D2= functional assay D3= family history Prior OR x P(D1|C5)/P(D1|C1) x P(D2|C5)/P(D2|C1) x P(D3|C5)/P(D3|C1)
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Establishing a consortium of LQTS experts to adjudicate VUS
Identifying a funding mechanism Partnering with commercial and research genetic testing Partnering with SADS and findmyvariant.org Ideally review QTc, symptoms, functional data, family history
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