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bioRxiv · 10.1101/2025.11.26.690754

Haplotype-resolved diploid genome inference on pangenome graphs

Chandra, G.·Doan, W. T.·Gibney, D.
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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. ResultsWe 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 5.7x lower than PanGenie + Beagle (4.88%), 7.9x lower than VG (6.77%), and 13.2x 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 Implementationhttps://github.com/gsc74/DipGenie.

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Chandra, G., Doan, W. T., Gibney, D.. 2025-11-27. Haplotype-resolved diploid genome inference on pangenome graphs. https://doi.org/10.1101/2025.11.26.690754

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