Phasing Alleles Improves Network Inference with Allopolyploids

Accurately reconstructing the evolutionary histories of polyploids remains a central challenge for understanding plant evolution and tree of life. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring such networks may be hampered by the complexities of homology determination in polyploid taxa. In this project, we present a novel pipeline that leverages recent advances in phasing algorithms to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where depth of coverage is typically high enough to phase entire loci. We provided an empirical demonstration of how phasing can provide insights into mode of polyploidization and improve network inference in Dryopteris ferns. We demonstrate that our pipeline (PATE: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids, and these data improved network estimates compared to using haplotype consensus assemblies, especially in reticulate complexes where there are multiple introgression events.