Unsupervised clustering analysis reveals global population structure of SARS-CoV-2

Li, Yawei; Liu, Qingyun; Zeng, Zexian; Luo, Yuan

Identifying the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Since we have little prior information about the newly emerged coronavirus, we applied a state-of-the-art unsupervised deep learning clustering algorithm to group 16,873 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. The varied proportions of the six clusters within different continents revealed specific geographical distributions. Comprehensive analysis indicated that genetic factors and human migration played an important role in shaping the specific geographical distribution of population. This study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses.