Estimating divergence times in large molecular phylogenies

Abstract

Molecular dating of species divergences has become an important means to add a temporal dimension to the Tree of Life. Increasingly larger datasets encompassing greater taxonomic diversity are becoming available to generate molecular timetrees by using sophisticated methods that model rate variation among lineages. However, the practical application of these methods is challenging because of the exorbitant calculation times required by current methods for contemporary data sizes, the difficulty in correctly modeling the rate heterogeneity in highly diverse taxonomic groups, and the lack of reliable clock calibrations and their uncertainty distributions for most groups of species. Here, we present a method that estimates relative times of divergences for all branching points (nodes) in very large phylogenetic trees without assuming a specific model for lineage rate variation or specifying any clock calibrations. The method (RelTime) performed better than existing methods when applied to very large computer simulated datasets where evolutionary rates were varied extensively among lineages by following autocorrelated and uncorrelated models. On average, RelTime completed calculations 1,000 times faster than the fastest Bayesian method, with even greater speed difference for larger number of sequences. This speed and accuracy will enable molecular dating analysis of very large datasets. Relative time estimates will be useful for determining the relative ordering and spacing of speciation events, identifying lineages with significantly slower or faster evolutionary rates, diagnosing the effect of selected calibrations on absolute divergence times, and estimating absolute times of divergence when highly reliable calibration points are available.bioinformatics | timescales | relaxed clocks T housands of research studies have reported the use of molecular dating techniques in establishing the timing of species divergences (e.g., refs. 1-5). With the availability of fast and cheap genome sequencing, molecular dating is being applied to increasingly larger datasets that span a much greater diversity of species and harbor extensive heterogeneity of evolutionary rates among lineages. This complexity poses many challenges that limit modern scientific investigations from truly leveraging the genome revolution. First, the application of the fastest molecular dating tools available already requires a very large amount of computational time for datasets containing only a few hundred sequences, which are modest for today's standards (6, 7). Second, current approaches require a priori selection of statistical distributions to model the heterogeneity of rates among branches in the evolutionary tree (e.g., autocorrelated versus uncorrelated rates, 8-12). Use of an incorrect statistical distribution is known to introduce significant bias in such analyses (10,(13)(14)(15). With increasingly larger datasets, it is unlikely that the same rate model will fit evolutionarily distant groups in the same large phylogeny, which exacerbates the...

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“…The divergence time between H. oligactis group and H. vulgaris group (72-132 million years ago, Mya) that was estimated by Schwentner and Bosch (2015) was used to calibrate the estimated times of divergence of the clusters. The analysis was conducted using the RelTime method (Tamura et al, 2012).…”