International travelers and genomics uncover a ‘hidden’ Zika outbreak

Nathan D. Grubaugh; Sharada Saraf; Karthik Gangavarapu; Alexander Watts; Amanda L. Tan; Rachel J. Oidtman; Jason T. Ladner; Glenn Oliveira; Nathaniel L. Matteson; Moritz U.G. Kraemer; Chantal B.F. Vogels; Aaron Hentoff; Deepit Bhatia; Danielle Stanek; Blake Scott; Vanessa Landis; Ian Stryker; Marshall R. Cone; Edgar W. Kopp; Andrew C. Cannons; Lea Heberlein-Larson; Stephen White; Leah D. Gillis; Michael J. Ricciardi; Jaclyn Kwal; Paola K. Lichtenberger; Diogo M. Magnani; David I. Watkins; Gustavo Palacios; Davidson H. Hamer; Lauren M. Gardner; T. Alex Perkins; Guy Baele; Kamran Khan; Andrea Morrison; Sharon Isern; Scott F. Michael; Kristian G. Andersen

To determine the temporal signal of the sequence dataset, a maximum likelihood (ML) phylogeny was first reconstructed with RAxML (Stamatakis, 2014) using the general time-reversible (GTR) nucleotide substitution model and gamma-distributed rates amongst sites (Guindon and Gascuel, 2003; Yang, 1994) . Then, a correlation between root-to-tip genetic divergence and date of sampling was conducted in TempEst (Guindon and Gascuel, 2003; Rambaut et al., 2016; Yang, 1994) . Timescaled phylogenetic trees were reconstructed using the Bayesian phylogenetic inference framework available in BEAST v1.10.2 . Accommodating phylogenetic uncertainty, we used an HKY+Γ4 nucleotide substitution model for each codon position, allowing for relative rates between these positions to be estimated, and an uncorrelated relaxed molecular clock model, with an underlying lognormal distribution (Drummond et al., 2006) , a non-parametric demographic prior (Gill et al., 2013) and otherwise default priors in BEAUti v1.10.2 . The MCMC analysis was run for 800 million iterations, sampling every 100,000th iteration, using the BEAGLE library v2.1.2 to accelerate computation (Ayres et al., 2012) . MCMC performance was inspected for convergence and for sufficient sampling using Tracer v.1.7.1 . After discarding the first 200 million iterations as burn-in, virus diffusion over time and space was summarised using a maximum clade credibility (MCC) tree using TreeAnnotator . Tree visualizations were generated with the Phylo (Talevich et al., 2012) module from Biopython and matplotlib (Hunter, 2007) . Raw MAFFT codon alignment data, PhyML tree, BEAST XML file, and BEAST MCC timestructured phylogeny can be found at: https://github.com/andersen-lab/paper_2018_cuba-travelzika.