2021/10/22 by Jingcheng Xu, Cécile Ané, Xu, Jingcheng +1 · 4 citations
Biochemistry, Genetics and Molecular Biology · Earth and Planetary Sciences · Mathematics · #Evolution and Paleontology Studies #FOS: Biological sciences #FOS: Mathematics #Genetic diversity and population structure #Genomics and Phylogenetic Studies #Populations and Evolution (q-bio.PE) #Statistics Theory (math.ST) #math.ST #q-bio.PE #stat.TH
paper · pdf · doi:10.48550/arxiv.2110.11814
openalex publication_date 2021/10/22 · openalex created_date 2021/11/08 · arxiv created 2022/06/25 · arxiv updated 2022/06/28 · openalex updated_date 2026/07/28
Phylogenetic networks extend phylogenetic trees to model non-vertical inheritance, by which a lineage inherits material from multiple parents. The computational complexity of estimating phylogenetic networks from genome-wide data with likelihood-based methods limits the size of networks that can be handled. Methods based on pairwise distances could offer faster alternatives. We study here the information that average pairwise distances contain on the underlying phylogenetic network, by characterizing local and global features that can or cannot be identified. For general networks, we clarify that the root and edge lengths adjacent to reticulations are not identifiable, and then focus on the class of zipped-up semidirected networks. We provide a criterion to swap subgraphs locally, such as 3-cycles, resulting in indistinguishable networks. We propose the "distance split tree", which can be constructed from pairwise distances, and prove that it is a refinement of the network's tree of blobs, capturing the tree-like features of the network. For level-1 networks, this distance split tree is equal to the tree of blobs refined to separate polytomies from blobs, and we prove that the mixed representation of the network is identifiable. The information loss is localized around 4-cycles, for which the placement of the reticulation is unidentifiable. The mixed representation combines split edges for 4-cycles, regular tree and hybrid edges from the semidirected network, and edge parameters that encode all information identifiable from average pairwise distances.