SvAnna: efficient and accurate pathogenicity prediction for coding and regulatory structural variants in long-read genome sequencing

Abstract

Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to limitations of existing technology. Recent technological advances such as long-read sequencing (LRS) enable more comprehensive detection of SVs, but approaches for clinical prioritization of candidate SVs are needed. Existing computational approaches do not specifically target LRS data, thereby missing a substantial proportion of candidate SVs, and do not provide a unified computational model for assessing all types of SVs. Structural Variant Annotation and Analysis (SvAnna) assesses all classes of SV and their intersection with transcripts and regulatory sequences in the context of topologically associating domains, relating predicted effects on gene function with clinical phenotype data. We show with a collection of 182 published case reports with pathogenic SVs that SvAnna places over 90% of pathogenic SVs in the top ten ranks. The interpretable prioritizations provided by SvAnna will facilitate the widespread adoption of LRS in diagnostic genomics.

Phenotype‐driven approaches to enhance variant prioritization and diagnosis of rare disease

“…Twenty‐eight of the 184 curated known SV diagnoses involve an SNV/indel in compound heterozygosity with an SV and in all cases Exomiser was able to detect both variants and prioritize the diagnosis effectively in the top 3 ranked candidates. The same phenopackets have already been assessed for SvAnna and AnnotSV using a different set of long‐read, pbsv called VCFs and showed 61% and 86% in the top and top 5 candidates for SvAnna and 60% and 65% for AnnotSV (Danis et al, 2021a).…”

“…SvAnna (Danis et al, 2021a) focuses on SVs called from long-read technologies. AnnotSV (Geoffroy et al, 2021) and 86% in the top and top 5 candidates for SvAnna and 60% and 65% for AnnotSV (Danis et al, 2021a).…”

“…The ability of Exomiser to prioritize known molecular diagnoses involving an SV is shown in Figure 4. Previously described phenopackets (https://phenopacket-schema.readthedocs.io/en/latest/index.html) representing curated phenotypic and pedigree data from the literature (Danis et al, 2021a) were used as input to Exomiser alongside corresponding VCFs containing the curated variant(s) added to a control WGS VCF file based on either short‐ or long‐read technologies. The former used an Illumina short‐read sample with SVs called using Manta and Canvas.…”

“…Despite these challenges, several phenotype-based prioritization tools have recently emerged that offer SV prioritization. SvAnna (Danis et al, 2021a) focuses on SVs called from long-read technologies. AnnotSV (Geoffroy et al, 2021) and 86% in the top and top 5 candidates for SvAnna and 60% and 65% for AnnotSV (Danis et al, 2021a).…”