One year of SARS-CoV-2: Genomic characterization of COVID-19 outbreak in Qatar
Preprint
- 20 May 2021
- preprint
- Published by Cold Spring Harbor Laboratory
Abstract
Qatar, a state that has a diverse population consisting mainly of foreign residents, has experienced a large COVID19 outbreak. In this study, we report on 2634 SARS-CoV-2 whole-genome sequences from infected patients in Qatar between March-2020 and March-2021, representing 1.5% of all positive cases in this period. Despite the restrictions on international travel, the viruses sampled from the populace of Qatar mirrored nearly the entire global population’s genomic diversity with nine predominant viral lineages that were sustained by local transmission chains and the emergence of mutations that are likely to have originated in Qatar. We reported an increased number of mutations and deletions in B.1.1.7 and B.1.351 lineages in a short period. These findings raise the imperative need to continue the ongoing genomic surveillance that has been an integral part of the national response to monitor the SARS-CoV-2 profile and re-emergence in Qatar.Keywords
Other Versions
- Published version: Version Frontiers in Cellular and Infection Microbiology, 11, preprints
This publication has 31 references indexed in Scilit:
- The coronavirus pandemic in five powerful chartsNature, 2020
- The proximal origin of SARS-CoV-2Nature Medicine, 2020
- The Novel Coronavirus Originating in Wuhan, ChinaJAMA, 2020
- A Novel Coronavirus from Patients with Pneumonia in China, 2019The New England Journal of Medicine, 2020
- An interactive web-based dashboard to track COVID-19 in real timeThe Lancet Infectious Diseases, 2020
- nCoV-2019 sequencing protocol v1Published by ZappyLab, Inc. ,2020
- ModelFinder: fast model selection for accurate phylogenetic estimatesNature Methods, 2017
- Epidemiology, Genetic Recombination, and Pathogenesis of CoronavirusesTrends in Microbiology, 2016
- Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen)Virus Evolution, 2016
- IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood PhylogeniesMolecular Biology and Evolution, 2014