Introduction: The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed extraordinary challenges to global health systems and economies. The virus's rapid evolution has resulted in several variants of concern (VOCs), including the highly transmissible Omicron variant, characterized by extensive mutations. In this study, we investigated the genetic diversity, population differentiation, and evolutionary dynamics of the Omicron VOC during the fifth wave of COVID-19 in Pakistan.
Methods: A total of 954 Omicron genomes sequenced during the fifth wave of COVID-19 in Pakistan were analyzed. A Bayesian framework was employed for phylogenetic reconstructions, molecular dating, and population dynamics analysis.
Results: Using a population genomics approach, we analyzed Pakistani Omicron samples, revealing low within-population genetic diversity and significant structural variation in the spike (S) protein. Phylogenetic analysis showed that the Omicron variant in Pakistan originated from two distinct lineages, BA.1 and BA.2, which were introduced from South Africa, Thailand, Spain, and Belgium. Omicron-specific mutations, including those in the receptor-binding domain, were identified. The estimated molecular evolutionary rate was 2.562E-3 mutations per site per year (95% HPD interval: 8.8067E-4 to 4.1462E-3). Bayesian skyline plot analysis indicated a significant population expansion at the end of 2021, coinciding with the global Omicron outbreak. Comparative analysis with other VOCs showed Omicron as a highly divergent, monophyletic group, suggesting a unique evolutionary pathway.
Conclusions: This study provides a comprehensive overview of Omicron's genetic diversity, genomic epidemiology, and evolutionary dynamics in Pakistan, emphasizing the need for global collaboration in monitoring variants and enhancing pandemic preparedness.
Keywords: COVID-19; Omicron; SARS-CoV-2; VOCs; genomic epidemiology.
Copyright © 2024 Razzaq, Disoma, Iqbal, Nisar, Hameed, Qadeer, Waqar, Mehmood, Gao, Khan and Xia.