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Yang, Q.* ; Sun, J.* ; Wang, X.* ; Wang, J.* ; Liu, Q.* ; Ru, J. ; Zhang, X.* ; Wang, S.* ; Hao, R.* ; Bian, P.* ; Dai, X.* ; Gong, M.* ; Zhang, Z.* ; Wang, A.* ; Bai, F.* ; Li, R.* ; Cai, Y.* ; Jiang, Y.*

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

Nat. Commun. 16:2406 (2025)

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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.

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