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

TL;DR

This study analyzes over 50,000 SARS-CoV-2 genomes to identify epistatic interactions between viral genes, revealing key gene pairs that could inform drug and vaccine development.

Contribution

It is the first comprehensive genome-wide epistasis analysis of SARS-CoV-2, identifying specific gene interactions using Direct Coupling Analysis and Quasi-Linkage Equilibrium assumptions.

Findings

Identified eight significant gene interactions involving ORF3a, nsp13, and others.

Discovered interactions between non-synonymous and synonymous mutations.

Revealed potential targets for understanding viral weaknesses.

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.