Comprehensive Structural Variant Detection: From Mosaic to Population-Level

Abstract

Long-read Structural Variation (SV) calling remains a challenging but highly accurate way to identify complex genomic alterations. Here, we present Sniffles2, which is faster and more accurate than state-of-the-art SV caller across different coverages, sequencing technologies, and SV types. Furthermore, Sniffles2 solves the problem of family- to population-level SV calling to produce fully genotyped VCF files by introducing a gVCF file concept. Across 11 probands, we accurately identified causative SVs around MECP2, including highly complex alleles with three overlapping SVs. Sniffles2 also enables the detection of mosaic SVs in bulk long-read data. As a result, we successfully identified multiple mosaic SVs across a multiple system atrophy patient brain. The identified SV showed a remarkable diversity within the cingulate cortex, impacting both genes involved in neuron function and repetitive elements. In summary, we demonstrate the utility and versatility of Sniffles2 to identify SVs from the mosaic to population levels.