Intronic Breakpoint Signatures Enhance Detection and Characterization of Clinically Relevant Germline Structural Variants

Abstract

The relevance of large copy number variants (CNVs) to hereditary disorders has been long recognized, and population sequencing efforts have chronicled many common structural variants (SVs). However, limited data are available on the clinical contribution of rare germline SVs. Here, a detailed characterization of SVs identified using targeted next-generation sequencing was performed. Across 50 genes associated with hereditary cancer and cardiovascular disorders, a minimum of 828 unique SVs were reported, including 584 fully characterized SVs. Almost 40% of CNVs were <5 kb, with one in three deletions impacting a single exon. Additionally, 36 mid-range deletions/duplications (50 to 250 bp), 21 mobile element insertions, 6 inversions, and 27 complex rearrangements were detected. This data set was used to model SV detection in a bioinformatics pipeline solely relying on read depth, which revealed that genome sequencing (30×) allows detection of 71%, a 500× panel only targeting coding regions 53%, and exome sequencing (100×) <20% of characterized SVs. SVs accounted for 14.1% of all unique pathogenic variants, supporting the importance of SVs in hereditary disorders. Robust SV detection requires an ensemble of variant-calling algorithms that utilize sequencing of intronic regions. These algorithms should use distinct data features representative of each class of mutational mechanism, including recombination between two sequences sharing high similarity, covariants inserted between CNV breakpoints, and complex rearrangements containing inverted sequences.