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2023 ACMG Annual Clinical Genetics Meeting Digital ...
Developing Bayesian graphical models to provide co ...
Developing Bayesian graphical models to provide continuous, probabilistic variant interpretation
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Pdf Summary
The document discusses the allele frequency model and the importance of variant interpretation in inferring the relationship between genetic variants and diseases. Traditionally, variants are classified into categories, but this leaves a large number of variants as uncertain. The document proposes using probabilistic graphical models (PGMs) to incorporate population allele frequency and clinical phenotype data in variant interpretation. PGMs are flexible Bayesian statistical frameworks that leverage domain expertise to perform probabilistic inference. The probability that a variant is pathogenic can be predicted using this approach. The document also mentions the Genome Aggregation Database (gnomAD) and Invitae's own research-eligible sample database as valuable sources of data for variant interpretation.<br /><br />The document explains the data generative process for allele counts and the use of additional features like constraint scores to enhance the model's accuracy. Pathogenicity likelihood can be estimated for new alleles with high accuracy. The document also includes graphs and examples to illustrate the interpretation process.<br /><br />The document emphasizes that probabilistic graphical modeling provides a suitable framework for modeling the complex relationships in variant interpretation. Domain-specific models like the allele frequency model and the clinical phenotype model can be combined into an overall variant interpretation model. This approach allows for more granular variant interpretations and the precise probability of pathogenicity can be reported instead of relying on the current five-category framework. The use of condition phenotypes learned from affected patients further enhances the prediction of variant pathogenicity.<br /><br />Overall, the document highlights the importance of probabilistic graphical modeling in variant interpretation and its potential to improve the accuracy and granularity of variant classifications.
Asset Subtitle
Presenting Author - Toby R. Manders, MD; Co-Author - Yuya Kobayashi, PhD; Co-Author - John Nicoludis, PhD; Co-Author - Arun Numpally, PhD; Co-Author - Flavia M. Facio, MS, CGC; Co-Author - Britt A. Johnson, PhD, FACMG; Co-Author - Keith Nykamp, PhD; Co-Author - Robert L. Nussbaum, MD; Co-Author - Alex Colavin, PhD;
Meta Tag
Clinical History
Genetic Diversity
Genetic Testing
Genomic Methodologies
Genotype-Phenotype Correlations
Natural History
Phenotype
Phenotypic delineation of disorders
Population Genetics
Risk Assessment
Co-Author
Yuya Kobayashi, PhD
Co-Author
John Nicoludis, PhD
Co-Author
Arun Numpally, PhD
Co-Author
Flavia M. Facio, MS, CGC
Co-Author
Britt A. Johnson, PhD, FACMG
Co-Author
Keith Nykamp, PhD
Co-Author
Robert L. Nussbaum, MD
Co-Author
Alex Colavin, PhD
Presenting Author
Toby R. Manders, MD
Keywords
allele frequency model
variant interpretation
genetic variants
probabilistic graphical models
population allele frequency
Bayesian statistical frameworks
pathogenicity prediction
Genome Aggregation Database
research-eligible sample database
variant pathogenicity
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