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2023 ACMG Annual Clinical Genetics Meeting Digital ...
Modeling Cellular Evidence: Scalable Approaches fo ...
Modeling Cellular Evidence: Scalable Approaches for Generating, Validating and Incorporating Data from High-Throughput Functional Assays to Improve Clinical Variant Interpretation
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Pdf Summary
The document discusses the challenges in interpreting variants of uncertain significance (VUS) in genetic testing and the potential of high-throughput functional assays to provide evidence for variant interpretation. Currently, commercial labs rely on aggregating functional data from academic literature, but the use of internal high-throughput functional assays in large lab settings has not been fully realized.<br /><br />The document presents the results of a study that used single-cell RNA sequencing (scRNA-seq) as a scalable platform for generating internal cellular evidence. The incorporation of internally-derived cellular modeling evidence increased reclassification rates by 4.6% for VUS. The study also shows that internal high-throughput functional assay data can produce highly performant, mechanistic evidence for clinical variant interpretation.<br /><br />The document describes a cellular evidence modeling strategy based on internal high-throughput functional data, where variant sets are introduced into cells and scRNA-seq is used to characterize the cellular effects of the variants. Machine-learning models are then trained and validated using gene expression-based features, and the predictions from these models are incorporated into the variant interpretation framework.<br /><br />The document emphasizes the importance of quality control in building models from both internal and external assay data. It also highlights the role of commercial laboratories in accelerating the generation, adoption, and clinical impact of evidence based on high-throughput functional data.<br /><br />Overall, the document suggests that incorporating internal high-throughput functional assay data can improve the interpretation of VUS in genetic testing and that commercial labs have the potential to contribute to this field.
Asset Subtitle
Presenting Author - Jason Reuter, PhD; Co-Author - Yuya Kobayashi, PhD; Co-Author - Flavia M. Facio, MS, CGC; Co-Author - Swaroop Aradhya; Co-Author - Britt A. Johnson, PhD, FACMG; Co-Author - Alex Colavin, PhD; Co-Author - Keith Nykamp, PhD;
Meta Tag
Bioinformatics
Genetic Testing
Genomic Methodologies
NextGen Sequencing
Co-Author
Yuya Kobayashi, PhD
Co-Author
Flavia M. Facio, MS, CGC
Co-Author
Swaroop Aradhya
Co-Author
Britt A. Johnson, PhD, FACMG
Co-Author
Alex Colavin, PhD
Co-Author
Keith Nykamp, PhD
Presenting Author
Jason Reuter, PhD
Keywords
variants of uncertain significance
genetic testing
high-throughput functional assays
interpretation
commercial labs
single-cell RNA sequencing
scRNA-seq
cellular evidence modeling
reclassification rates
clinical variant interpretation
© 2024 American College of Medical Genetics and Genomics. All rights reserved.
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