SPECIES TREE ESTIMATION UNDER JOINT MODELING OF COALESCENCE AND DUPLICATION: SAMPLE COMPLEXITY OF QUARTET METHODS

• 成果类型: Article
• 署名作者: Hill, Max; Legried, Brandon; Roch, Sebastien
• 署名单位: University of Wisconsin System; University of Wisconsin Madison; University of Michigan System; University of Michigan
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/22-AAP1799
• 发表日期: 2022
• 页码: 4681-4705
• 关键词: phase-transition; phylogenetic mixtures; reconstruction; INFORMATION; SEQUENCES; inference

摘要

We consider species tree estimation under a standard stochastic model of gene tree evolution that incorporates incomplete lineage sorting (as mod-eled by a coalescent process) and gene duplication and loss (as modeled by a branching process). Through a probabilistic analysis of the model, we derive sample complexity bounds for widely used quartet-based inference methods that highlight the effect of the duplication and loss rates in both subcritical and supercritical regimes.