Early detection of emerging viral variants through analysis of community structure of coordinated substitution networks
Fatemeh Mohebbi,
Alex Zelikovsky,
Serghei Mangul,
Gerardo Chowell and
Pavel Skums (pavel.skums@uconn.edu)
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Fatemeh Mohebbi: Georgia State University
Alex Zelikovsky: Georgia State University
Serghei Mangul: University of Southern California
Gerardo Chowell: Georgia State University
Pavel Skums: Georgia State University
Nature Communications, 2024, vol. 15, issue 1, 1-16
Abstract:
Abstract The emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47304-6
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DOI: 10.1038/s41467-024-47304-6
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