Bayesian Evaluation of Temporal Signal in Measurably Evolving Populations

Abstract

Phylogenetic methods can use the sampling times of molecular sequence data to calibrate the molecular clock, enabling the estimation of evolutionary rates and timescales for rapidly evolving pathogens and data sets containing ancient DNA samples. A key aspect of such calibrations is whether a sufficient amount of molecular evolution has occurred over the sampling time window, that is, whether the data can be treated as having come from a measurably evolving population. Here we investigate the performance of a fully Bayesian evaluation of temporal signal (BETS) in sequence data. The method involves comparing the fit to the data of two models: a model in which the data are accompanied by the actual (heterochronous) sampling times, and a model in which the samples are constrained to be contemporaneous (isochronous). We conducted simulations under a wide range of conditions to demonstrate that BETS accurately classifies data sets according to whether they contain temporal signal or not, even when there is substantial among-lineage rate variation. We explore the behaviour of this classification in analyses of five empirical data sets: modern samples of A/H1N1 influenza virus, the bacterium Bordetella pertussis, coronaviruses from mammalian hosts, ancient DNA from Hepatitis B virus and mitochondrial genomes of dog species. Our results indicate that BETS is an effective alternative to other tests of temporal signal. In particular, this method has the key advantage of allowing a coherent assessment of the entire model, including the molecular clock and tree prior which are essential aspects of Bayesian phylodynamic analyses.

Direct RNA sequencing and early evolution of SARS-CoV-2

“…In order to estimate the evolutionary rate and time of origin of SARS-CoV-2, we carried out phylogenetic analyses in BEAST v1.101 [M10], with a curated set of 66 complete and high quality SARS-CoV-2 genomes with date of collection data, as available on February 10th from GISAID and GenBank (Supplementary Table 2). Temporal signal was assessed using BETS [M11]. Initially we determined whether the evolutionary signal and time over which the genome data were collected was sufficient to calibrate the molecular clock, allowing for the evolutionary rate and timescale of the outbreak to be inferred.…”

Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic

“…In such cases, even moderate rate variation among long deep phylogenetic branches will significantly impact expected root-to-tip divergences over a sampling time range that represents only a small fraction of the evolutionary history (Trova et al, 2015). However, formal testing using marginal likelihood estimation (Duchene et al, 2019) does not reject the absence of temporal signal in all three data sets (Table S1), albeit without strong support in favor of temporal signal (log Bayes factor support of 3, 10, and 3 for NRR1, NRR2, and NRA3 respectively).…”

“…To estimate nonsynonymous over synonymous rate ratios for the concatenated coding genes, we used the empirical Bayes 'Renaissance counting' procedure (Lemey et al, 2012). Temporal signal was tested using a recently developed marginal likelihood estimation procedure (Duchene et al, 2019) ( Table S1).…”

Accommodating individual travel history, global mobility, and unsampled diversity in phylogeography: a SARS-CoV-2 case study

“…Upon visualizing root-to-tip divergence as a function of sampling time using TempEst 22 based on an ML tree inferred with IQ-TREE 23 , we removed one potential outlier. We formally tested for temporal signal using BETS24 . The final 284 genomes were sampled from 28 different countries,…”