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Mol Biol Evol. 2019 Oct 22. pii: msz236. doi: 10.1093/molbev/msz236. [Epub ahead of print]

Reliable confidence intervals for RelTime estimates of evolutionary divergence times.

Tao Q1,2, Tamura K3,4, Mello B5, Kumar S1,2,6.

Author information

1
Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania.
2
Department of Biology, Temple University, Philadelphia, Pennsylvania.
3
Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Tokyo, Japan.
4
Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University, Hachioji, Tokyo, Japan.
5
Department of Genetics, Federal University of Rio de Janeiro, RJ, Brazil.
6
Center Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.

Abstract

Confidence intervals (CIs) depict the statistical uncertainty surrounding evolutionary divergence time estimates. They capture variance contributed by the finite number of sequences and sites used in the alignment, deviations of evolutionary rates from a strict molecular clock in a phylogeny, and uncertainty associated with clock calibrations. Reliable tests of biological hypotheses demand reliable CIs. However, current non-Bayesian methods may produce unreliable CIs because they do not incorporate rate variation among lineages and interactions among clock calibrations properly. Here, we present a new analytical method to calculate CIs of divergence times estimated using the RelTime method, along with an approach to utilize multiple calibration uncertainty densities in these analyses. Empirical data analyses showed that the new methods produce CIs that overlap with Bayesian highest posterior density (HPD) intervals. In the analysis of computer-simulated data, we found that RelTime CIs show excellent average coverage probabilities, i.e., the actual time is contained within the CIs with a 95% probability. These developments will encourage broader use of computationally-efficient RelTime approaches in molecular dating analyses and biological hypothesis testing.

PMID:
31638157
DOI:
10.1093/molbev/msz236

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