Author: Maciej F Boni; Philippe Lemey; Xiaowei Jiang; Tommy Tsan-Yuk Lam; Blair Perry; Todd Castoe; Andrew Rambaut; David L Robertson
Title: Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic Document date: 2020_3_31
ID: h2uc7ria_50
Snippet: Time-measured phylogenetic reconstruction was performed using a Bayesian approach implemented in BEAST (Suchard et al., 2018) . When the genomic data included both coding and noncoding regions we used a single GTR+Γ substitution model; for concatenated coding genes we partitioned the alignment by codon positions and specified an independent GTR+Γ model for each partition with a separate gamma model to accommodate among-site rate variation. We u.....
Document: Time-measured phylogenetic reconstruction was performed using a Bayesian approach implemented in BEAST (Suchard et al., 2018) . When the genomic data included both coding and noncoding regions we used a single GTR+Γ substitution model; for concatenated coding genes we partitioned the alignment by codon positions and specified an independent GTR+Γ model for each partition with a separate gamma model to accommodate among-site rate variation. We used an uncorrelated relaxed clock model with a lognormal distribution for all data sets, except for the low-diversity SARS data for which we specified a strict molecular clock model. For the HCoV-OC43, MERS-CoV, and SARS data sets we specified flexible skygrid coalescent tree priors. In the absence of any reasonable prior knowledge on the tMRCA of the sarbecovirus data sets (which is required for the grid specification in a skygrid model), we specified a simpler constant size population prior. As informative rate priors for the analysis of the sarbecovirus data sets, we used two different normal prior distributions: one with a mean of 0.00078 and a standard deviation of 0.0003 and one with a mean of 0.00024 and standard deviation of 0.0001. These means are based on the mean rates estimated for MERS-CoV and HCoV-OC43 respectively, while the standard deviations are set ten times larger than the empirical standard deviations to allow more prior uncertainty and avoid strong bias ( Supplementary Fig. 2 ).
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