Presented By: DCMB Seminar Series
Department of Computational Medicine & Bioinformatics || Weekly Seminar Series
Presenter: Rebecca Torene, Associate Director of Genomics Research | Data Science at GeneDx, "Structural variant breakpoint detection in a clinical diagnostic laboratory"
Abstract:
Structural variants (SVs) are a source of pathogenic variants in a clinical referral population, however, they are often under-reported due to technical limitations of detection and difficulty with clinical interpretation. For example, mobile element insertions (MEIs) are estimated to lead to a positive finding in 1 out of 1000 rare genetic disease cases, yet the numbers are far lower in clinical diagnostic laboratories. Targeted NGS with short insert size libraries, unlike genome sequencing, will have very few discordant read pairs to indicate the presence of an SV. We, therefore, developed an SV detection tool called SCRAMble (Soft Clipped Read Alignment Mapper) to identify SV breakpoints in targeted NGS.
We applied SCRAMble to a prospective clinical referral cohort for exome sequencing to identify deletions and MEIs. We also applied SCRAMble to a hereditary cancer panel assay for the identification of a large inversion involving the MSH2 gene that causes Lynch syndrome. Adding breakpoint detection to clinical targeted sequencing identifies positive findings which were missed by prior testing and by other variant callers. Detecting breakpoints allows for more precise interpretation and for more targeted confirmation assays. By applying SV breakpoint detection, we are able to diagnose ~0.3% more cases. While this is a modest gain in diagnostic yield, for the patients and families involved, a positive diagnosis, even after prior testing, can have a meaningful impact on their lives.
Zoom link: https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09
Structural variants (SVs) are a source of pathogenic variants in a clinical referral population, however, they are often under-reported due to technical limitations of detection and difficulty with clinical interpretation. For example, mobile element insertions (MEIs) are estimated to lead to a positive finding in 1 out of 1000 rare genetic disease cases, yet the numbers are far lower in clinical diagnostic laboratories. Targeted NGS with short insert size libraries, unlike genome sequencing, will have very few discordant read pairs to indicate the presence of an SV. We, therefore, developed an SV detection tool called SCRAMble (Soft Clipped Read Alignment Mapper) to identify SV breakpoints in targeted NGS.
We applied SCRAMble to a prospective clinical referral cohort for exome sequencing to identify deletions and MEIs. We also applied SCRAMble to a hereditary cancer panel assay for the identification of a large inversion involving the MSH2 gene that causes Lynch syndrome. Adding breakpoint detection to clinical targeted sequencing identifies positive findings which were missed by prior testing and by other variant callers. Detecting breakpoints allows for more precise interpretation and for more targeted confirmation assays. By applying SV breakpoint detection, we are able to diagnose ~0.3% more cases. While this is a modest gain in diagnostic yield, for the patients and families involved, a positive diagnosis, even after prior testing, can have a meaningful impact on their lives.
Zoom link: https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09
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