Constructing an Epistatic Network from Hepatitis C Viral Protein Sequence Data using Algorithmic Methods

Abstract

Epistasis, where one genetic mutation modifies the effect of another, is crucial for understanding complex traits, including disease risk and treatment response. In hepatitis C virus (HCV), a web of interactions among mutations dictates viral behavior and host engagement. To decode this complexity, we introduce a computational method that constructs an epistatic network linking viral mutations observed across patients. Our approach begins by (1) creating a mutation occurrence matrix from viral sequences and (2) statistically identifying significant pairwise epistatic interactions. The critical third step (3) builds a dense cluster map from the network, moving beyond simple visualization to extract functionally relevant modules. This cluster analysis is central to our method, as it isolates groups of tightly co-occurring and interacting mutations. These dense clusters are hypothesized to represent emergent viral haplotypes, a unique combinatorial code that may govern infectivity, immune evasion, and drug resistance. By rigorously analyzing cluster topology and density, we directly investigate the relationship between network architecture and haplotype formation. This allows us to identify high- order epistatic patterns that could inform novel treatment strategies and deepen the mechanistic understanding of HCV. This research provides a framework for elucidating high-order genetic interactions in HCV, setting the stage for advanced studies in viral evolution. Refining this cluster-driven analysis will clarify how epistasis shapes viral phenotypes, ultimately translating these insights into improved patient outcomes.