Poster Presentation 12th Australasian Virology Society Meeting 2024

Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data (#234)

Brian Lee 1 , Ahmed Abdul Quadeer 2 3 , Saqib Sohail 3 , Elizabeth Finney 1 , Faraz Ahmed 2 4 , Matthew McKay 2 4 , John Barton 1 5 6
  1. Department of Physics and Astronomy, University of California, Riverside, Riverside, USA
  2. Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC, Australia
  3. Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China
  4. Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, VIC, Australia
  5. Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, USA
  6. Department of Computational and Systems Biology , University of Pittsburgh School of Medicine, Pittsburgh , USA

New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could facilitate outbreak control efforts and highlight new variants that warrant further study. Here we develop an analytical path integral model that infers the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that substantially affect the transmission rate, both within and outside the Spike protein. The strongly selected mutations we infer for SARS-CoV-2 are supported by experimental evidence regarding their effects on neutralizing antibody escape, T cell/immune evasion, replication/infectivity, cell entry, and structure/cleavage. Importantly, our model detects lineages with increased transmission even at low frequencies. As an example, we infer significant transmission advantages for the Alpha, Delta, and Omicron variants shortly after their appearances in regional data, when their local frequencies were only around 1-2%. Our model thus facilitates the rapid identification of variants and mutations that affect transmission from genomic surveillance data.

  1. Lee, B. et al. Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data. 2021.12.31.21268591 Preprint at https://doi.org/10.1101/2021.12.31.21268591 (2022).