2025/11/27 by Ghanshyam Chandra, William T. Doan, Daniel Gibney · 1 voice
Biochemistry, Genetics and Molecular Biology · #Genetic Associations and Epidemiology #Genomics and Phylogenetic Studies #Genomics and Rare Diseases
paper · pdf · doi:10.1101/2025.11.26.690754
openalex publication_date 2025/11/27 · openalex created_date 2025/11/28 · openalex updated_date 2026/07/14
Abstract Recent algorithmic advancements have shown how to utilize pangenome graphs in combination with the haplotype reconstruction framework of Li and Stephens to accurately reconstruct a haplotype from a reference pangenome graph and a set of input reads. However, significant work remains in developing techniques that utilize a pangenome graph to obtain a pair of phased haplotypes called a diploid pair. Results We introduce new problem formulations and scalable algorithms for inferring phased diploid genomes from a pangenome graph and a set of input reads. We implement them in our tool DipGenie . The key idea is to jointly optimize genotyping and phasing along global paths through the pangenome graph, guided by a biologically motivated recombination budget that constrains inferred haplotypes to plausible mosaics of reference haplotypes. We evaluate DipGenie on real Illumina short-read data from the highly polymorphic MHC region in 22 leave-one-out diploid experiments, benchmarking against three tools that also operate on graph structures: VG , which samples haplotypes directly from the pangenome graph, and PanGenie + Beagle and Paragraph + Beagle , which derive local graphs from a VCF panel for per-site genotyping and delegate phasing to a statistical method. At full coverage, DipGenie achieves a geometric mean switch error rate (SER) of 0.86%, which is 5.7 × lower than PanGenie + Beagle (4.88%), 7.9 × lower than VG (6.77%), and 13.2 × lower than Paragraph + Beagle (11.35%). For structural variant calling, DipGenie leads with a geometric mean F1-score of 0.571, compared to 0.470 ( PanGenie + Beagle ), 0.450 ( VG ), and 0.379 ( Paragraph + Beagle ). These advantages hold at every coverage level tested. Availability and Implementation https://github.com/gsc74/DipGenie .