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2023 ACMG Annual Clinical Genetics Meeting Digital ...
SNP FASST3, an adaptive algorithmic approach for a ...
SNP FASST3, an adaptive algorithmic approach for accurate mosaic detection of CNV and LOH spanning technologies
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Bionano Genomics has developed the SNP-FASST3 algorithm as part of their NxClinical software to accurately detect low-level mosaic copy-number variant (CNV) and loss-of-heterozygosity (LOH) changes. The algorithm was tested on simulated datasets representing mosaic gain and loss events of varying sizes and allele fractions. It effectively detected mosaic and non-mosaic instabilities while reducing false positive segmentation by introducing new configurable "mosaic" states. Additionally, NxClinical automatically assessed the pathogenicity of the identified events based on the 2019 ACMG/ClinGen CNV interpretation guidelines.<br /><br />The detection of mosaic changes, whether inherited or acquired, is crucial as they are associated with congenital anomalies and cancer. The research simulated whole genome sequencing data and used the SNP-FASST3 algorithm for CNV calling in Bionano NxClinical. The algorithm demonstrated an overall sensitivity of 94.2% in detecting CNV events, with improvements in sensitivity for low-level allele fraction detection through manual review. However, there were a few cases where the algorithm did not detect certain events at a 10% allele fraction, suggesting the need for further optimization.<br /><br />Bionano's NxClinical software incorporates adaptable state transitions and a dynamic binning approach to detect copy-number and allelic imbalances across different technologies. The latest version of the software, SNP-FASST3, uses a Hidden Markov Model (HMM) with mosaic state changes for gains and losses, as well as a plateau pseudo-distribution to enhance sensitivity for detecting mosaic CNV changes.<br /><br />The software also enables automatic pre-classification of copy-number variants against the ACMG CNV scoring guidelines, providing filtering and sorting capabilities for analysis of massive deep coverage data from NGS or OGM techniques.<br /><br />In conclusion, SNP-FASST3 accurately detects mosaic and non-mosaic instabilities, and it is important to include a manual review step to enhance sensitivity and accuracy for detecting mosaicism. Accurate detection of low-level allele fraction copy number events is vital as they can impact clinically relevant regions and be associated with pathogenesis.
Asset Subtitle
Co-Author - Daniel Saul, MS; Presenting Author - Sam Dougaparsad, PhD; Co-Author - Megan Roytman, PhD; Co-Author - Westley Sherman, PhD; Co-Author - Soheil Shams, PhD; Co-Author - Shalini Verma, MS; Co-Author - Neil Miller, PhD;
Meta Tag
Clinical Applications of Molecular Cytogenetics
Genomic Methodologies
Microarray
Molecular Cytogenetics
Variant Detection
Co-Author
Daniel Saul, MS
Co-Author
Megan Roytman, PhD
Co-Author
Westley Sherman, PhD
Co-Author
Soheil Shams, PhD
Co-Author
Shalini Verma, MS
Co-Author
Neil Miller, PhD
Presenting Author
Sam Dougaparsad, PhD
Keywords
Bionano Genomics
SNP-FASST3 algorithm
NxClinical software
copy-number variant
loss-of-heterozygosity
mosaic changes
allele fraction
ACMG/ClinGen CNV interpretation guidelines
Hidden Markov Model
massive deep coverage data
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