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Activity Number: 287 - Advanced Stochastic Models and Inference Methods for Large-Scale Phylogenetics
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #304911 Presentation
Title: Fitness-Dependent Birth-Death Models for Phylodynamic Inference of Adaptive Evolution
Author(s): David Rasmussen* and Tanja Stadler
Companies: North Carolina State University and ETH Zurich
Keywords: Phylogenetics; Molecular evolution; Birth-death models; Fitness inference; Viruses

Beneficial and deleterious mutations cause the fitness of lineages to vary across a phylogeny and thereby shape its branching structure. While standard phylogenetic models do not allow mutations to feedback and shape trees, birth-death models can account for this feedback by letting the fitness of lineages depend on their type. To date, these multi-type birth-death models have only been applied to cases where a lineage's fitness is determined by a single character state. We extend these models to track the fitness of a lineage and sequence evolution at multiple sites. This approach remains computationally tractable by tracking the fitness and the genotype of lineages probabilistically in an approximate manner. Although approximate, we show that we can accurately estimate the fitness of lineages and even specific mutation effects from phylogenies. We apply this approach to estimate the population-level fitness effects of mutations previously identified to increase Ebola virus infectivity in humans.

Authors who are presenting talks have a * after their name.

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