Full Bayesian Comparative Phylogeography from Genomic Data.

Publisher: Oxford University Press Country of Publication: England NLM ID: 9302532 Publication Model: Print Cited Medium: Internet ISSN: 1076-836X (Electronic) Linking ISSN: 10635157 NLM ISO Abbreviation: Syst Biol Subsets: MEDLINE

Publication: 2009- : Oxford : Oxford University Press
Original Publication: Washington, D.C., USA : Society of Systematic Biologists, [1992-

Bayes Theorem* ; Phylogeography*; Classification/*methods ; Genome/*genetics; Animals ; Computer Simulation ; Genomics ; Lizards/classification ; Lizards/genetics ; Philippines ; Phylogeny

A challenge to understanding biological diversification is accounting for community-scale processes that cause multiple, co-distributed lineages to co-speciate. Such processes predict non-independent, temporally clustered divergences across taxa. Approximate-likelihood Bayesian computation (ABC) approaches to inferring such patterns from comparative genetic data are very sensitive to prior assumptions and often biased toward estimating shared divergences. We introduce a full-likelihood Bayesian approach, ecoevolity, which takes full advantage of information in genomic data. By analytically integrating over gene trees, we are able to directly calculate the likelihood of the population history from genomic data, and efficiently sample the model-averaged posterior via Markov chain Monte Carlo algorithms. Using simulations, we find that the new method is much more accurate and precise at estimating the number and timing of divergence events across pairs of populations than existing approximate-likelihood methods. Our full Bayesian approach also requires several orders of magnitude less computational time than existing ABC approaches. We find that despite assuming unlinked characters (e.g., unlinked single-nucleotide polymorphisms), the new method performs better if this assumption is violated in order to retain the constant characters of whole linked loci. In fact, retaining constant characters allows the new method to robustly estimate the correct number of divergence events with high posterior probability in the face of character-acquisition biases, which commonly plague loci assembled from reduced-representation genomic libraries. We apply our method to genomic data from four pairs of insular populations of Gekko lizards from the Philippines that are not expected to have co-diverged. Despite all four pairs diverging very recently, our method strongly supports that they diverged independently, and these results are robust to very disparate prior assumptions.
(© The Author(s) 2018. Published by Oxford University Press.)

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Keywords: Bayesian model choice; Dirichlet process prior; biogeography; phylogeography

Dryad 10.5061/dryad.4b3j2bj

Date Created: 20180922 Date Completed: 20190430 Latest Revision: 20231011

20240829

PMC6472446

10.1093/sysbio/syy063

30239868