A comparison of methods for estimating substitution rates from ancient DNA sequence data
AuthorTong, KJ; Duchene, DA; Duchene, S; Geoghegan, JL; Ho, SYW
Source TitleBMC Evolutionary Biology
PublisherBIOMED CENTRAL LTD
University of Melbourne Author/sDuchene Garzon, Sebastian
AffiliationMicrobiology and Immunology
Document TypeJournal Article
CitationsTong, K. J., Duchene, D. A., Duchene, S., Geoghegan, J. L. & Ho, S. Y. W. (2018). A comparison of methods for estimating substitution rates from ancient DNA sequence data. BMC EVOLUTIONARY BIOLOGY, 18 (1), https://doi.org/10.1186/S12862-018-1192-3.
Access StatusOpen Access
BACKGROUND: Phylogenetic 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. RESULTS: Our 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. CONCLUSIONS: We 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.
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