Temporal signal and the phylodynamic threshold of SARS-CoV-2

Abstract

The ongoing SARS-CoV-2 outbreak marks the first time that large amounts of genome sequence data have been generated and made publicly available in near real-time. Early analyses of these data revealed low sequence variation, a finding that is consistent with a recently emerging outbreak, but which raises the question of whether such data are sufficiently informative for phylogenetic inferences of evolutionary rates and time scales. The phylodynamic threshold is a key concept that refers to the point in time at which sufficient molecular evolutionary change has accumulated in available genome samples to obtain robust phylodynamic estimates. For example, before the phylodynamic threshold is reached, genomic variation is so low that even large amounts of genome sequences may be insufficient to estimate the virus’s evolutionary rate and the time scale of an outbreak. We collected genome sequences of SARS-CoV-2 from public databases at 8 different points in time and conducted a range of tests of temporal signal to determine if and when the phylodynamic threshold was reached, and the range of inferences that could be reliably drawn from these data. Our results indicate that by February 2nd 2020, estimates of evolutionary rates and time scales had become possible. Analyses of subsequent data sets, that included between 47 to 122 genomes, converged at an evolutionary rate of about 1.1 × 10−3 subs/site/year and a time of origin of around late November 2019. Our study provides guidelines to assess the phylodynamic threshold and demonstrates that establishing this threshold constitutes a fundamental step for understanding the power and limitations of early data in outbreak genome surveillance.

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“…The outlier sequences were removed before the phylogenetic analysis performed in BEAST 1.10.4 [ 33 ]. Bayesian time-scaled trees were inferred for each dataset using a strict clock applying a fixed mean clock rate of 0.8 × 10 −3 as used by other authors [ 7 , 34 ]. We also applied the Bayesian coalescent Skyline model and the GTR + G4 + I nucleotide substitution model.…”

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“…A root-to-tip regression of genetic divergence against sampling time was performed on the resultant maximum likelihood tree using TempEst v.1.5.3 15 . Due to the quantity of sequences and because it did not display obvious temporal signal, we obtained a time-scaled phylogenetic tree using LSDv0.3 16 with a fixed evolutionary rate of 1×10 -3 subs/site/year as estimated previously 17 , and with the sampling dates as additional calibration information.…”

“…We fixed the time-tree described above for the analysis. This method is broadly similar to that used to infer geographic movement of the virus in Belgium 17 .…”

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“…Despite a relatively slow evolutionary rate, the "phylodynamic threshold" for SARS-CoV-2 was reached relatively early 9 , meaning that sufficient divergence had accumulated over the sampling time range to infer time-calibrated phylogenies and the underlying transmission processes that generate such trees, including spatiotemporal spread. However, sampling bias presents a critical challenge for popular discrete trait ancestral reconstruction procedures 10,11 .…”