2021/12/24 by Hong-Li Zeng, Yue Liu, Zeng, Hong-Li +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Evolution and Genetic Dynamics #FOS: Biological sciences #Genetic Mapping and Diversity in Plants and Animals #Genomics (q-bio.GN) #Genomics and Phylogenetic Studies #q-bio.GN
paper · pdf · doi:10.48550/arxiv.2112.12957
15 pages, 9 figures
openalex publication_date 2021/12/24 · arxiv created 2022/06/06 · arxiv updated 2022/06/07 · openalex created_date 2022/11/27 · openalex updated_date 2026/08/01
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.