Global analysis of more than 50,000 SARS-Cov-2 genomes reveals epistasis between 8 viral genes

Abstract

Genome-wide epistasis analysis is a powerful tool to infer gene interactions, which can guide drug and vaccine development and lead to a deeper understanding of microbial pathogenesis. We have considered all complete SARS-CoV-2 genomes deposited in the GISAID repository until \textbf{four} different cut-off dates, and used Direct Coupling Analysis together with an assumption of Quasi-Linkage Equilibrium to infer epistatic contributions to fitness from polymorphic loci. We find \textbf{eight} interactions, of which three between pairs where one locus lies in gene ORF3a, both loci holding non-synonymous mutations. We also find interactions between two loci in gene nsp13, both holding non-synonymous mutations, and four interactions involving one locus holding a synonymous mutation. Altogether we infer interactions between loci in viral genes ORF3a and nsp2, nsp12 and nsp6, between ORF8 and nsp4, and between loci in genes nsp2, nsp13 and nsp14. The paper opens the prospect to use prominent epistatically linked pairs as a starting point to search for combinatorial weaknesses of recombinant viral pathogens.

arXiv comment22 pages, 11 pages

References in corpus (12)

• Weak pairwise correlations imply strongly correlated network states in a neural population
• Direct-coupling analysis of residue co-evolution captures native contacts across many protein families
• Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models
• High-dimensional Ising model selection using ${\ell }_1$-regularized logistic regression
• Inverse statistical problems: from the inverse Ising problem to data science
• Inverse Statistical Physics of Protein Sequences: A Key Issues Review
• Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences
• Inverse Ising inference using all the data
• Cyclic dominance and biodiversity in well-mixed populations
• Competition between recombination and epistasis can cause a transition from allele to genotype selection
• Statistical Genetics and Evolution of Quantitative Traits
• Inferring genetic fitness from genomic data

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• Ancestral Sequence Reconstruction for Co-evolutionary models
• Classification of SARS-CoV-2 Variants through The Epistatical Circos Plots with Convolutional Neural Networks