Bayesian Inference of Sampled Ancestor Trees for Epidemiology and Fossil Calibration

By Alexandra Gavryushkina, David Welch, Tanja Stadler, Alexei J. Drummond. PLoS Comput. Biol., 2014

338 citations recorded by the source corpus.

Phylogenetic analyses which include fossils or molecular sequences that are sampled through time require models that allow one sample to be a direct ancestor of another sample. As previously available phylogenetic inference tools assume that all samples are tips, they do not allow for this possibility. We have developed and implemented a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to infer what we call sampled ancestor trees, that is, trees in which sampled individuals can be direct ancestors of other sampled individuals. We use a family of birth-death models where individuals may remain in the tree process after sampling, in particular we extend the birth-death skyline model [Stadler et al., 2013] to sampled ancestor trees. This method allows the detection of sampled ancestors as well as estimation of the probability that an individual will be removed from the process when it is sampled. We show that even if sampled ancestors are not of specific interest in an analysis, failing to account for them leads to significant bias in parameter estimates. We also show that sampled ancestor birth-death models where every sample comes from a different time point are non-identifiable and thus require one parameter to be known in order to infer other parameters. We apply our phylogenetic inference accounting for sampled ancestors to epidemiological data, where the possibility of sampled ancestors enables us to identify individuals that infected other individuals after being sampled and to infer fundamental epidemiological parameters. We also apply the method to infer divergence times and diversification rates when fossils are included along with extant species samples, so that fossilisation events are modelled as a part of the tree branching process. Such modelling has many advantages as argued in the literature. The sampler is available as an open-source BEAST2 package (https://github.com/CompEvol/sampled-ancestors).

Subfields: Artificial Intelligence, Molecular Biology, Paleontology

View Paper: https://www.semanticscholar.org/paper/b068d78f70daa0af325e2cca821e0f2a7c3583d8

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Citations in the corpus

Bayesian estimation of fossil phylogenies and the evolution of early to middle Paleozoic crinoids (Echinodermata) (2017, methodology): “Gavryushkina et al. (2014) demonstrated that sampled ancestors should be accounted for when estimating phylogenies, even when ancestral morphotaxa are not of specific interest in the analysis, because not including them introduces biases in parameter estimation.”
bModelTest: Bayesian phylogenetic site model averaging and model comparison (2015, methodology): “…in any analysis where trees are estimated based on nucleotide sequences, such as multi-species coalescent analysis [29, 30], various forms of phylogeographical analyses, sampled ancestor analysis [31], demographic reconstruction using coalescent [32], birth death skyline analysis [33], et cetera .”