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

文献类型： 外文期刊

作者： Yang, Qimeng 1 ; Sun, Jianfeng 2 ; Wang, Xinyu 1 ; Wang, Jiong 1 ; Liu, Quanzhong 3 ; Ru, Jinlong 4 ; Zhang, Xin 3 ; Wang, Sizhe 3 ; Hao, Ran 3 ; Bian, Peipei 1 ; Dai, Xuelei 1 ; Gong, Mian 1 ; Zhang, Zhuangbiao 1 ; Wang, Ao 1 ; Bai, Fengting 1 ; Li, Ran 1 ; Cai, Yudong 1 ; Jiang, Yu 1 ;

作者机构： 1.Northwest A&F Univ, Coll Anim Sci & Technol, Key Lab Anim Genet Breeding & Reprod Shaanxi Prov, Yangling, Shaanxi, Peoples R China

2.Univ Oxford, Botnar Res Ctr, Nuffield Dept Orthopaed Rheumatol & Musculoskeleta, Oxford, England

3.Northwest A&F Univ, Coll Informat Engn, Yangling, Shaanxi, Peoples R China

4.Helmholtz Ctr Munich, Inst Virol, German Res Ctr Environm Hlth, Neuherberg, Germany

5.Yazhouwan Natl Lab, Sanya, Hainan, Peoples R China

6.Chinese Acad Agr Sci CAAS, Inst Anim Sci, State Key Lab Anim Biotech Breeding, Beijing, Peoples R China

期刊名称：NATURE COMMUNICATIONS （ 影响因子：15.7； 五年影响因子：17.2 ）

ISSN：

年卷期： 2025 年 16 卷 1 期

页码：

收录情况： SCI

摘要： 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 30x 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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