An Efficient Coalescent Epoch Model for Bayesian Phylogenetic Inference

• Published in: Systematic biology Vol. 71; no. 6; pp. 1549 - 1560
• Main Author: Bouckaert, Remco R
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 12.10.2022
• Subjects: Algorithms; Bayes Theorem; Bayesian analysis; COVID-19; Humans; Markov Chains; Mathematical models; Models, Genetic; Monte Carlo Method; Phylogenetics; Phylogeny; Population; Population number; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Software
• ISSN: 1063-5157; 1076-836X; 1076-836X
• DOI: 10.1093/sysbio/syac015

We present a two-headed approach called Bayesian Integrated Coalescent Epoch PlotS (BICEPS) for efficient inference of coalescent epoch models. Firstly, we integrate out population size parameters, and secondly, we introduce a set of more powerful Markov chain Monte Carlo (MCMC) proposals for flexing and stretching trees. Even though population sizes are integrated out and not explicitly sampled through MCMC, we are still able to generate samples from the population size posteriors. This allows demographic reconstruction through time and estimating the timing and magnitude of population bottlenecks and full population histories. Altogether, BICEPS can be considered a more muscular version of the popular Bayesian skyline model. We demonstrate its power and correctness by a well-calibrated simulation study. Furthermore, we demonstrate with an application to SARS-CoV-2 genomic data that some analyses that have trouble converging with the traditional Bayesian skyline prior and standard MCMC proposals can do well with the BICEPS approach. BICEPS is available as open-source package for BEAST 2 under GPL license and has a user-friendly graphical user interface.[Bayesian phylogenetics; BEAST 2; BICEPS; coalescent model.]