Abstract
BackgroundPhylogenetic analysis of DNA from modern and ancient samples allows the reconstruction of important demographic and evolutionary processes. A critical component of these analyses is the estimation of evolutionary rates, which can be calibrated using information about the ages of the samples. However, the reliability of these rate estimates can be negatively affected by among-lineage rate variation and non-random sampling. Using a simulation study, we compared the performance of three phylogenetic methods for inferring evolutionary rates from time-structured data sets: regression of root-to-tip distances, least-squares dating, and Bayesian inference. We also applied these three methods to time-structured mitogenomic data sets from six vertebrate species.ResultsOur results from 12 simulation scenarios show that the three methods produce reliable estimates when the substitution rate is high, rate variation is low, and samples of similar ages are not all grouped together in the tree (i.e., low phylo-temporal clustering). The interaction of these factors is particularly important for least-squares dating and Bayesian estimation of evolutionary rates. The three estimation methods produced consistent estimates of rates across most of the six mitogenomic data sets, with sequence data from horses being an exception.ConclusionsWe recommend that phylogenetic studies of ancient DNA sequences should use multiple methods of inference and test for the presence of temporal signal, among-lineage rate variation, and phylo-temporal clustering in the data.
Highlights
Phylogenetic analysis of DNA from modern and ancient samples allows the reconstruction of important demographic and evolutionary processes
We investigate the impacts of these factors on rate estimates made using RTT regression, least-squares dating, and Bayesian phylogenetic analysis
We found a positive correlation between phylogenetic stemminess and the spread of median posterior rate estimates in conditions of high rate variation and high clustering (P < 0.001; Additional file 7: Figure S6)
Summary
Phylogenetic analysis of DNA from modern and ancient samples allows the reconstruction of important demographic and evolutionary processes. We compared the performance of three phylogenetic methods for inferring evolutionary rates from time-structured data sets: regression of root-to-tip distances, least-squares dating, and Bayesian inference. Time-structured sequence data are common in studies of rapidly evolving genomes, such as those of pathogens [4] They can be obtained by sequencing DNA from ancient samples of animals, plants, and fungi [5]. When relying on the tip dates for calibration, an important condition is that the population must be ‘measurably evolving’ [6], whereby the sampling window is wide enough to capture an appreciable amount of genetic change This depends on the evolutionary rate, which varies across genes and species [7, 8]. Assembling data sets with sufficient temporal structure can be difficult to achieve for slowly evolving organisms such as vertebrates [9, 10]
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