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Substitution spectra of SARS-CoV-2 genome reveals insights into the evolution of variants across the pandemic
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  • Javaria Ashraf,
  • Ali Raza Bukhari,
  • Akbar Kanji,
  • Tulaib Iqbal,
  • Waqasuddin Khan,
  • Zahra Hasan
Javaria Ashraf
The Aga Khan University Hospital
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Ali Raza Bukhari
The Aga Khan University Hospital
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Akbar Kanji
The Aga Khan University Hospital
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Tulaib Iqbal
The Aga Khan University Hospital
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Waqasuddin Khan
The Aga Khan University Department of Paediatrics and Child Health
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Zahra Hasan
The Aga Khan University Hospital

Corresponding Author:[email protected]

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Abstract

Background: Changing morbidity and mortality from COVID-19 has been associated with the emergence of new SARS-CoV-2 variants. Whereby, acquisition of mutations in the Spike glycoprotein enhanced host receptor binding, cell entry and antibody escape. Understanding these can help predict the impact of these changes. We used genome sequence data to investigate mutation rates and entropy of SARS-CoV-2 during pandemic surges between 2020 and 2022. Methods: 1,637 SARS-CoV-2 genomes from Pakistan were analyzed using the Augur phylogenetic pipeline. Substitution rates and entropy of genomes were calculated year wise and, entropy in the Spike gene was compared for 2020, 2021 and 2022 (defined as periods A, B and C). Central Findings: In period A, G clades were predominant and SARS-CoV-2 genome substitution rate was 6.06 x 10 -4 per site per year. In period B, Delta variant were dominant and substitution rates increased to 9.74 x 10 -4. In period C, Omicron variants dominated with substitution rates at 5.02 x 10 -4. The rate of genome-wide entropy was the highest during B particularly, in the Spike gene such as, E484K and K417N. During C, genome-wide mutations were increased whilst entropy was reduced. Conclusions: The highest SARS-CoV-2 genome substitution rates in 2021 were associated with the Delta wave, which had the greatest morbidity and mortality. These stabilized during the Omicron wave in 2022, when COVID-19 numbers were high mortality was lower. Assessment of SARS-CoV-2 evolution should be monitored together with phylogeographical analysis can help predict future outbreaks and guide public health interventions.