Varigraph: an accurate and widely applicable pangenome graph-based variant genotyper for diploid and polyploid genomes

Abstract

Graph pangenome references can address single-reference bias, thereby enhancing the accuracy of variant genotyping and empowering downstream applications in population genetics and quantitative genetics. However, existing pangenome-based genotyping methods struggle with large or complex pangenome graphs, particularly in polyploid genomes. Here, we introduce Varigraph, an algorithm that leverages the comparison of unique and repetitive k-mers between variant sites and short reads for genotyping both small and large variants. Varigraph outperforms current state-of-the-art linear and graph-based genotypers across non-human genomes while maintaining comparable accuracy in human genomes. By employing an efficient data structure, Varigraph achieves higher accuracy in repetitive regions while managing computational costs for large datasets. Notably, Varigraph is the first tool capable of effectively utilizing pangenome graphs for genotyping autopolyploids, enabling precise determination of allele dosage. This work provides a robust and accurate solution for genotyping non-human genomes and will facilitate genomic studies of polyploid crops.