SVhet: towards accurate detection of germline heterozygous deletions using short reads.

backgroundAccurate structural variant detection from short-read sequencing data remains challenged by false positives, particularly for heterozygous deletions where reduced allelic support and coverage-based detection methods are ambiguous. Existing SV genotyping and filtering approaches suffer from significant recall reductions, dependencies on additional pre-computed resources, or restriction to depth-based signals that overlook read level evidence.

resultsHere we present SVhet, a novel computational framework that leverages the heterozygosity patterns detected from different read evidences to identify false heterozygous deletions. Comprehensive benchmarking using 31 Human Genome Structural Variation Consortium Phase 3 samples demonstrated SVhet's ability to further reduce false positives while maintaining baseline recall. Hybrid approach of duphold and SVhet achieved up to 60% reduction in false positive counts while preserving recall. We also showed SVhet to be computationally efficient that can complete a whole genome structural variant callset under 5 min using 4 CPU cores. SVhet is available under a permissive MIT license via https://github.com/snakesch/SVhet .

conclusionSVhet provides an accurate and efficient solution for evaluating heterozygous deletions derived from short read sequencing data. SVhet can be used as a standalone tool or in conjunction with other filtering tools such as duphold. Importantly, it does not require additional variant sets, and can operate with minimal compute. Altogether, SVhet adds to the current effort to achieve accurate structural variant detection using short reads.