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

Area of Science:

• Genomics
• Bioinformatics
• Population Genetics

Background:

• Comparing complete genome assemblies is key to understanding genetic differences.
• Existing tools struggle with large, repetitive plant genomes due to aligner or organism specificity.

Purpose of the Study:

• Introduce Structural Variants Genotyping of Assemblies on Population scales (SVGAP), a novel pipeline for population-scale structural variant (SV) discovery, genotyping, and annotation.
• Address limitations of current tools for large and repetitive plant genomes.

Main Methods:

• Developed SVGAP, a pipeline for SV discovery, genotyping, and annotation from high-quality genome assemblies.
• Conducted extensive benchmarks using simulated SV datasets across individual, population, and phylogenetic contexts.
• Applied SVGAP to 26 maize genomes.

Main Results:

• SVGAP demonstrates superior performance in SV discovery compared to existing tools.
• SVGAP effectively genotypes SVs in large assembled genomes, generating fully genotyped VCF files.
• Discovered hidden genomic diversity in maize centromeres, linked to LTR-retrotransposon insertions.

Conclusions:

• SVGAP is a powerful tool for population-level SV analysis in large plant genomes.
• The pipeline facilitates pangenome construction and exploration of previously uncharacterized genomic regions.
• Highlights the utility of SVGAP for uncovering complex genomic diversity, such as in maize centromeres.