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

Qimeng Yang, Jianfeng Sun, Xinyu Wang, Jiong Wang, Quanzhong Liu, Jinlong Ru, Xin Zhang, Sizhe Wang, Ran Hao, Peipei Bian, Xuelei Dai, Mian Gong, Zhuangbiao Zhang, Ao Wang, Fengting Bai, Ran Li, Yudong Cai () and Yu Jiang ()
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Qimeng Yang: Northwest A&F University
Jianfeng Sun: University of Oxford
Xinyu Wang: Northwest A&F University
Jiong Wang: Northwest A&F University
Quanzhong Liu: Northwest A&F University
Jinlong Ru: Helmholtz Centre Munich - German Research Centre for Environmental Health
Xin Zhang: Northwest A&F University
Sizhe Wang: Northwest A&F University
Ran Hao: Northwest A&F University
Peipei Bian: Northwest A&F University
Xuelei Dai: Northwest A&F University
Mian Gong: Northwest A&F University
Zhuangbiao Zhang: Northwest A&F University
Ao Wang: Northwest A&F University
Fengting Bai: Northwest A&F University
Ran Li: Northwest A&F University
Yudong Cai: Northwest A&F University
Yu Jiang: Northwest A&F University

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-57756-z

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