SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants

Structural variations (SVs) are diverse forms of genetic alterations and drive a wide range of human diseases. Accurately genotyping SVs, particularly occurring at repetitive genomic regions, from short-read sequencing data remains challenging. Here, we introduce SVLearn, a machine-learning approach for genotyping bi-allelic SVs. It exploits a dual-reference strategy to engineer a curated set of genomic, alignment, and genotyping features based on a reference genome in concert with an allele-based alternative genome. Using 38,613 human-derived SVs, we show that SVLearn significantly outperforms four state-of-the-art tools, with precision improvements of up to 15.61% for insertions and 13.75% for deletions in repetitive regions. On two additional sets of 121,435 cattle SVs and 113,042 sheep SVs, SVLearn demonstrates a strong generalizability to cross-species genotype SVs with a weighted genotype concordance score of up to 90%. Notably, SVLearn enables accurate genotyping of SVs at low sequencing coverage, which is comparable to the accuracy at 30× coverage. Our studies suggest that SVLearn can accelerate the understanding of associations between the genome-scale, high-quality genotyped SVs and diseases across multiple species. Accurately genotyping structural variations (SVs) from short-read sequencing data is challenging. Here, the authors introduce SVLearn for precise genotyping of bi-allelic SVs, demonstrating robust cross-species generalizability across multiple coverage levels.