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2023 ACMG Annual Clinical Genetics Meeting Digital ...
Predicting variant classification by modeling the ...
Predicting variant classification by modeling the effects of functional and case study data in
RUNX1
variants of uncertain significance
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This study focuses on predicting the classification of variants in the RUNX1 gene based on functional and case study data. Currently, over 50% of deposited RUNX1 variants in ClinVar are considered variants of uncertain significance (VUS) due to a lack of information for classification. The primary means of further classifying these VUS is through functional evidence or identification of additional probands or family segregation, but this can be challenging due to the rarity of the disorder. <br /><br />The researchers aimed to identify potential RUNX1 variants that would be significantly impacted by the addition of hypothetical evidence to prioritize them for directed functional testing. They used a deep generative model called EVE to predict variant pathogenicity and determine which ACMG/AMP codes to apply for each variant. They defined a threshold EVE score to distinguish potentially benign versus pathogenic variants.<br /><br />Using this approach, all VUS variants can be reclassified with the addition of hypothetical functional and case study data. The researchers found that the Runt-homology Domain of the RUNX1 gene produces the most pathogenic variants compared to the rest of the gene. Functional testing was identified as the most targeted way to reclassify a variant.<br /><br />The researchers plan to use their methodology to prioritize RUNX1 VUS for inter-lab collaborative functional testing. They also aim to apply this in silico methodology to predict the classification of variants that have conflicting benign and pathogenic ACMG/AMP criteria. Additionally, they hope to apply this methodology to other genes.<br /><br />In conclusion, this study demonstrates the potential of using functional and case study data to reclassify VUS variants in the RUNX1 gene. This approach can help prioritize variants for further testing and improve variant classification accuracy.
Asset Subtitle
Presenting Author - Mancy Shah, MSc; Co-Author - Winslow Johnson, MS; Co-Author - Lucy A. Godley, MD, PhD; Co-Author - David Wu, MD, PhD;
Meta Tag
Bioinformatics
Counseling
Exome sequencing
Genome sequencing
Methodology
Risk Assessment
Sequencing
Co-Author
Winslow Johnson, MS
Co-Author
Lucy A. Godley, MD, PhD
Co-Author
David Wu, MD, PhD
Presenting Author
Mancy Shah, MSc
Keywords
RUNX1 gene
variant classification
variants of uncertain significance
functional evidence
case study data
deep generative model
EVE
pathogenicity prediction
ACMG/AMP codes
reclassification
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