A class of identifiable phylogenetic birth-death models
| Autor: | Jonathan Terhorst, Brandon Legried |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
education.field_of_study
Class (set theory) Phylogenetic tree Population Dynamics Population Dimension (graph theory) Parturition Biological Evolution Models Biological Markov Chains Birth–death process Death Econometrics Piecewise Humans Tree (set theory) education Constant (mathematics) Pandemics Phylogeny Mathematics |
| DOI: | 10.1101/2021.10.04.463015 |
| Popis: | In a striking result, Louca and Pennell (2020) recently proved that a large class of phylogenetic birth-death models are statistically unidentifiable from lineage-through-time (LTT) data: any pair of sufficiently smooth birth and death rate functions is “congruent” to an infinite collection of other rate functions, all of which have the same likelihood for any LTT vector of any dimension. As Louca and Pennell argue, this fact has distressing implications for the thousands of studies that have utilized birth-death models to study evolution.In this paper, we qualify their finding by proving that an alternative and widely used class of birth-death models is indeed identifiable. Specifically, we show that piecewise constant birth-death models can, in principle, be consistently estimated and distinguished from one another, given a sufficiently large extant time tree and some knowledge of the present-day population. Subject to mild regularity conditions, we further show that any unidentifiable birth-death model class can be arbitrarily closely approximated by a class of identifiable models. The sampling requirements needed for our results to hold are explicit, and are expected to be satisfied in many contexts such as the phylodynamic analysis of a global pandemic. |
| Databáze: | OpenAIRE |
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