Coupling adaptive molecular evolution to phylodynamics using fitness-dependent birth-death models
Abstract
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 sequence evolution at multiple sites. This approach remains computationally tractable by tracking the genotype and fitness of lineages probabilistically in an approximate manner. Although approximate, we show that we can accurately estimate the fitness of lineages and site-specific mutational fitness effects from phylogenies. We apply this approach to estimate the population-level fitness effects of mutations in Ebola and influenza virus, and compare our estimates with in vitro fitness measurements for these mutations.
Data availability
All data and code required to reproduce our Ebola analysis in its entirety is available at https://github.com/davidrasm/Lumiere/tree/master/ebola. The sequence alignment along with the timecalibrated molecular phylogeny we used for our analysis were downloaded from https://github.com/ebov/space-time/tree/master/Data. Dataset S3 of Lee et al. 2018 was downloaded from https://www.pnas.org/highwire/filestream/822898/field_highwire_adjunct_files/3/pnas.1806133115.sd03.xlsx.
Article and author information
Author details
Funding
Seventh Framework Programme (European Research Commission)
- Tania Stadler
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2019, Rasmussen & Stadler
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Evolutionary Biology
- Genetics and Genomics
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- Evolutionary Biology
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