  We use Direct Coupling Analysis (DCA) to determine epistatic interactions
between loci of variability of the SARS-CoV-2 virus, segmenting genomes by
month of sampling. We use full-length, high-quality genomes from the GISAID
repository up to October 2021, in total over 3,500,000 genomes. We find that
DCA terms are more stable over time than correlations, but nevertheless change
over time as mutations disappear from the global population or reach fixation.
Correlations are enriched for phylogenetic effects, and in particularly
statistical dependencies at short genomic distances, while DCA brings out links
at longer genomic distance. We discuss the validity of a DCA analysis under
these conditions in terms of a transient Quasi-Linkage Equilibrium state. We
identify putative epistatic interaction mutations involving loci in Spike.

Genomics