Chi-Square and Mutual Information Profiling of SARS-CoV-2 Spike Mutation Clusters in a Comprehensive Global Dataset

This dataset presents the results of a large-scale statistical analysis of mutation co-occurrence patterns in the SARS-CoV-2 spike protein derived from 158,342 globally distributed viral genomes . A Chi-Square test of independence and Mutual Information (MI) analysis were applied to assess non-random linkage among mutations. The most significant co-occurring pair was S:A1015S & S:L24S , with a p-value ≈ 0.0, indicating near-zero probability of random co-occurrence. This suggests potential functional or structural interaction, possibly enhancing viral fitness, immune escape, or receptor binding. Tools used: ivar MAFFT SAMtools BCFtools Python (SciPy, pandas, sklearn) Mutation tracking and lineage classification were performed using GVAtlas