Bayesian molecular dating: opening up the black box

Biol Rev Camb Philos Soc. 2018 May;93(2):1165-1191. doi: 10.1111/brv.12390. Epub 2017 Dec 15.

Abstract

Molecular dating analyses allow evolutionary timescales to be estimated from genetic data, offering an unprecedented capacity for investigating the evolutionary past of all species. These methods require us to make assumptions about the relationship between genetic change and evolutionary time, often referred to as a 'molecular clock'. Although initially regarded with scepticism, molecular dating has now been adopted in many areas of biology. This broad uptake has been due partly to the development of Bayesian methods that allow complex aspects of molecular evolution, such as variation in rates of change across lineages, to be taken into account. But in order to do this, Bayesian dating methods rely on a range of assumptions about the evolutionary process, which vary in their degree of biological realism and empirical support. These assumptions can have substantial impacts on the estimates produced by molecular dating analyses. The aim of this review is to open the 'black box' of Bayesian molecular dating and have a look at the machinery inside. We explain the components of these dating methods, the important decisions that researchers must make in their analyses, and the factors that need to be considered when interpreting results. We illustrate the effects that the choices of different models and priors can have on the outcome of the analysis, and suggest ways to explore these impacts. We describe some major research directions that may improve the reliability of Bayesian dating. The goal of our review is to help researchers to make informed choices when using Bayesian phylogenetic methods to estimate evolutionary rates and timescales.

Keywords: evolutionary models; model selection; molecular clocks; phylogenetics; priors; relaxed clocks.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • Bayes Theorem
  • Computational Biology / methods
  • Evolution, Molecular*
  • Models, Genetic
  • Phylogeny*