New framework for recombination and adaptive evolution analysis with application to the novel coronavirus SARS-CoV-2

Abstract

The 2019 novel coronavirus (SARS-CoV-2) has spread rapidly worldwide and was declared a pandemic by the WHO in March 2020. The evolution of SARS-CoV-2, either in its natural reservoir or in the human population, is still unclear, but this knowledge is essential for effective prevention and control. We propose a new framework to systematically identify recombination events, excluding those due to noise and convergent evolution. We found that several recombination events occurred for SARS-CoV-2 before its transfer to humans, including a more recent recombination event in the receptor-binding domain. We also constructed a probabilistic mutation network to explore the diversity and evolution of SARS-CoV-2 after human infection. Clustering results show that the novel coronavirus has diverged into several clusters that cocirculate over time in various regions and that several mutations across the genome are fixed during transmission throughout the human population, including D614G in the S gene and two accompanied mutations in ORF1ab. Together, these findings suggest that SARS-CoV-2 experienced a complicated evolution process in the natural environment and point to its continuous adaptation to humans. The new framework proposed in this study can help our understanding of and response to other emerging pathogens.

Keywords: SARS-CoV-2; adaptation; evolution; mutation network; recombination.

Figure 1

Phylogenetic and recombination analysis of SARS-CoV-2. (A) Phylogenetic tree for the 53 betacoronaviruses based on genomic sequences. (B) Recombination regions identified in this study (regions with color different from backbone). Recombinant events with further divergence time estimation are starred. The red dash line indicates the bootstrap value of 0.8 for the support of recombination.

Figure 2

Mutation network of SARS-CoV-2 and the dynamic change of clusters. (A) Each node represents a cluster, including many sequences that were classified into the same cluster based on the clustering procedure. The node size is scaled to the number of sequences included. The links were colored according to the color of its origin. High-frequency mutations between main clusters were noted along the links with the color of their corresponding clusters. (B) The global proportion of clusters over time. The vertical axis indicates each week since the last week of 2019. (C) The proportion of clusters in different regions over time. Only clusters with a proportion of greater than 0.1 are shown.