Despite the appearance of variant SARS-CoV-2 viruses with altered receptorbinding or antigenic phenotypes, traditional methods for detecting adaptive evolution from sequence data do not pick up strong signals of positive selection. Here, we present a new method for identifying adaptive evolution on short evolutionary time scales with densely-sampled populations. We apply this method to SARS-CoV-2 to perform a comprehensive analysis of adaptively-evolving regions of the genome. We find that spike S1 is a focal point of adaptive evolution, but also identify positively-selected mutations in other genes that are sculpting the evolutionary trajectory of SARS-CoV-2. Protein-coding mutations in S1 are temporally-clustered and, in 2021, the ratio of nonsynonymous to synonymous divergence in S1 is more than 4 times greater than in the equivalent influenza HA1 subunit.
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