Population-level structural variant characterization using pangenome graphs

Population-level structural variant (SV) profiling is crucial in the era of pangenomes. However, identifying SVs from genome assemblies and pangenome graphs remains a substantial challenge. Here we present Swave, a sequence-to-image, deep learning-based method that accurately resolves both simple and complex SVs, along with their population characteristics, from assembly-derived pangenome graphs. Swave introduces 'projection waves' to summarize the dotplot images that capture mapping patterns between reference and SV-indicating alleles in the pangenome. Then, a recurrent neural network distinguishes true SV signals from background noise introduced by genomic repeats. Swave demonstrates superior performance in both SV-type classification and genotyping compared with existing methods. When applied to healthy cohorts and rare-disease cohorts, Swave reveals complex and polymorphic SV patterns across human populations and identifies potentially pathogenic SVs. These advancements will facilitate the creation of comprehensive population-level SV catalogs, deepening our understanding of SVs in genetic diversity and disease associations.