Population genomic insights into the evolution of the SARS-CoV-2 Omicron variant

A thorough understanding of the patterns of population subdivision of a pathogen can prevent disease spread. For SARS-CoV-2, the availability of millions of genomes makes this task analytically challenging. Our study used population genomic methods and identified subtle subdivisions within the Omicron variant, in addition to that captured by the Pango lineage. Further, some of the identified clusters of the Omicron variant revealed statistically significant signatures of selection or expansion revealing the role of microevolutionary processes in the spread of the virus. These are crucial information for policy makers as preventive measures can be designed to mitigate further spread based on a holistic understanding of the variability of the virus and evolutionary processes aiding its spread. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement B.C. acknowledges the startup funding from Trivedi School of Biosciences (TSB), Ashoka University, India and K.M.G. acknowledges the support from the DBT-Ramalingaswami Fellowship (No. BT/HRD/35/02/2006). V.L. was supported by TSB fellowship. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced are available online at GISAID. We gratefully acknowledge the following Authors from the Originating laboratories responsible for obtaining the specimens and the Submitting laboratories where genetic sequence data were generated and shared via GISAID Initiative, on which this research is based. A full acknowledgement table can be found in Appendix Table S1.