Department of Mathematics and Statistics, University of Alaska, Fairbanks, AK, USA.
Department of Mathematics, California State University San Bernadino, San Bernadino, CA, USA.
Bull Math Biol. 2024 Jul 25;86(9):110. doi: 10.1007/s11538-024-01339-4.
When hybridization or other forms of lateral gene transfer have occurred, evolutionary relationships of species are better represented by phylogenetic networks than by trees. While inference of such networks remains challenging, several recently proposed methods are based on quartet concordance factors-the probabilities that a tree relating a gene sampled from the species displays the possible 4-taxon relationships. Building on earlier results, we investigate what level-1 network features are identifiable from concordance factors under the network multispecies coalescent model. We obtain results on both topological features of the network, and numerical parameters, uncovering a number of failures of identifiability related to 3-cycles in the network. Addressing these identifiability issues is essential for designing statistically consistent inference methods.
当杂交或其他形式的侧向基因转移发生时，种间进化关系由系统发育网络而非系统发育树来更好地表示。尽管对这些网络的推断仍然具有挑战性，但最近提出的几种方法基于四分体一致性因子——即与从物种中采样的基因相关的树显示可能的 4 分类群关系的概率。基于早期的结果，我们研究了在网络多物种合并模型下，一致性因子可识别哪些网络一级特征。我们获得了网络拓扑特征和数值参数的结果，揭示了与网络中的 3-循环相关的一些可识别性失败。解决这些可识别性问题对于设计统计一致的推断方法至关重要。
