Annotation of structural variants with reported allele frequencies and related metrics from multiple datasets using SVAFotate

Abstract

BackgroundIdentification of impactful genetic variants from DNA sequencing data relies on increasingly detailed filtering strategies to isolate the small subset of variants that are more likely to underlie a disease phenotype. Datasets reflecting population allele frequencies of different types of variants have been demonstrated as powerful filtering tools, especially in the context of rare disease analysis. While such population-scale allele frequency datasets now exist for structural variants (SVs), it remains a challenge to match SV calls between multiple datasets and thereby correctly estimate the population allele frequency of a putative SV. ResultsWe introduce SVAFotate, a software tool for SV matching that enables the annotation of SVs with variant allele frequency and related information. These annotations are derived from known SV datasets which are incorporated by SVAFotate. As a result, VCF files annotated by SVAFotate offer a variety of annotations to aid in the stratification of SVs as common or rare in the broader human population. ConclusionsHere we demonstrate the use of SVAFotate in the classification of SVs with regards to their population frequency and illustrate how annotations provided by SVAFotate can be used to filter and prioritize SVs. Lastly, we detail how best to utilize these SV annotations in the analysis of genetic variation in studies of rare disease.