Accurate, Scalable Structural Variant Genotyping in Complex Genomes at Population Scales

作者：Ming Hu, Penglong Wan, Chengjie Chen, Shuyuan Tang, Jiahao Chen, Liang Wang, Mahul Chakraborty, Yongfeng Zhou, Jinfeng Chen, Brandon S. Gaut, J. J. Emerson, Yi Liao · 发表于：Molecular Biology and Evolution · 年份：2025 · DOI：10.1093/molbev/msaf180 · 被引用次数：6 · 研究领域：Genetic Mapping and Diversity in Plants and Animals、Plant Virus Research Studies、Genetics and Plant Breeding

Comparisons of complete genome assemblies offer a direct procedure for characterizing all genetic differences among them. However, existing tools are often limited to specific aligners or optimized for specific organisms, narrowing their applicability, particularly for large and repetitive plant genomes. Here, we introduce Structural Variants Genotyping of Assemblies on Population scales (SVGAP), a pipeline for structural variant (SV) discovery, genotyping, and annotation from high-quality genome assemblies at the population level. Through extensive benchmarks using simulated SV datasets at individual, population, and phylogenetic contexts, we demonstrate that SVGAP performs favorably relative to existing tools in SV discovery. Additionally, SVGAP is one of the few tools to address the challenge of genotyping SVs within large assembled genome samples, and it generates fully genotyped VCF files. Applying SVGAP to 26 maize genomes revealed hidden genomic diversity in centromeres, driven by abundant insertions of centromere-specific LTR-retrotransposons. The output of SVGAP is well-suited for pangenome construction and facilitates the interpretation of previously unexplored genomic regions.