数字农科院2.0

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： 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;Yu Jiang

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期刊名称： Nature Communications

ISSN： 2041-1723

年卷期： 2025 年 16 卷 1 期

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收录情况： SCIE（2025版）

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 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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