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  • Community structure and temporal dynamics of SARS-CoV-2 epistatic network allow for early detection of emerging variants with altered phenotypes
Title: Community structure and temporal dynamics of SARS-CoV-2 epistatic network allow for early detection of emerging variants with altered phenotypes
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.  more » « less
Award ID(s):
2415564
PAR ID:
10574684
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Lecture Notes in Computer Science
Date Published:
Format(s):
Medium: X
Institution:
University of Connecticut
Sponsoring Org:
National Science Foundation
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    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. 
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  2. ; ; ; ; ; ; ; ; (, Bioinformatics)
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    Mutheneni, Srinivasa Rao (Ed.)
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