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.

精准重建多倍体的演化历史，始终是解析植物演化与生命之树研究的核心难题。尽管系统发育网络（phylogenetic network）能够为阐明多倍体支系间的演化关系提供重要洞见，但对这类网络进行推断往往会因多倍体类群中同源性判定的复杂性而受阻。本研究提出一套全新的分析流程，该流程借助当前分型算法（phasing algorithm）领域的最新进展，可对多倍体的等位基因实现可靠分型。该流程尤其适配目标富集测序数据（target enrichment data）——这类数据的测序深度通常足以对完整基因座完成分型。我们以鳞毛蕨属（Dryopteris）蕨类为实证材料，展示了分型分析如何揭示多倍化模式，并优化系统发育网络的推断流程。本研究证明，我们开发的分析流程（PATE：Phased Alleles from Target Enrichment data，即基于目标富集测序数据的分型等位基因）能够从二倍体与多倍体样本中获取高比例的分型基因座；相较于采用单倍型共识组装的分析方案，此类分型数据可显著优化系统发育网络的推断结果，尤其适用于存在多次渐渗事件的网状演化复合体。