METHODS
Dengue virus serotypes (DENV-1, DENV-2, DENV-3, and DENV-4) WGS with
available country and year of sampling were downloaded from GenBank and
nextstrain 19 (Supplementary material). All dengue
virus WGS were aligned by the MAFFT v7 and visually inspected with
AliView v1.26. The best-fitting nucleotide substitution (GTR) model was
selected using a hierarchical likelihood ratio, Akaike information
criterion, and Bayesian information criterion tests with Model Finder in
IQ-TRE web server (http://iqtree.cibiv.univie.ac.at/). Dengue virus
maximum likelihood phylogenetic tree was inferred according to the
best-fitting model using IQ-TRE web server
(http://iqtree.cibiv.univie.ac.at/). We used this tree to obtain
root-to-tip regressions in TempEst v1.5.
Time-scaled phylogenetic trees, evolutionary rates, and demographic
histories of dengue virus WGS were evaluated using the Bayesian
coalescent framework implemented in BEAST v2.6.2, which uses a Markov
Chain Monte Carlo (MCMC) sampling method to obtain posterior
distributions of tree topologies and parameter estimates20. Bifurcating nodes with posterior probability
greater than 0.95 were considered statistically well supported. For each
run of 900 million of MCMC, the marginal likelihood was estimated via
path sampling (PS) and stepping stone (SS) methods and the resulting
Bayes Factors (BF) (ratio of marginal likelihoods) was used to select
the best-fitting clock/demographic model. The models can be compared to
evaluate the strength of evidence against the null hypothesis (H0)
defined in the following way: 2lnBF < 2 indicates no evidence
against H0; 2-6, weak evidence; 6-10: strong evidence, and
>10 very strong evidence. Both SS and PS estimators
indicated the uncorrelated lognormal (UCLN) relaxed molecular clock
(Bayes Factor = 30.3) as the best-fitted model to the dataset under
analysis. Besides, we have used the GTR substitution model.
MCMC were run for 900 million generations to ensure stationary and
adequate effective sample size (ESS) for all statistical parameters.
Tracer v.1.6 software was used to diagnose MCMC, adjust initial burn-in.
Bayesian coalescent analyses were performed to estimate the viral
dynamics and the time to the Most Recent Common Ancestor (tMRCA). The
time-scale calibration was based on the isolation date of samples.
Uncertainty in parameter estimates was evaluated in the 95% highest
posterior density (HPD 95%) interval. TreeAnnotator v1.8.2 was used to
summarize the maximum clade credibility (MCC) tree from the posterior
distribution of trees.
Dengue virus phylogeographic analysis, incorporating both spatial and
temporal information, was performed with BEAST v2.6.2 using a discrete
trait, symmetric substitution model with Bayesian stochastic search
variable selection (BSSVS). The reversible discrete Bayesian
phylogeographic model with a continuous-time Markov chain rate reference
prior was performed. The number of viral migrations between locations
was estimated using ‘Markov Jump’ counts of location-state transitions
along with the posterior tree distribution. Migratory events across time
were summarized using the SPREAD v.1.0.7. BFs>3 were
considered as well supported diffusion rates constituting the migration
graph.