Statistical inference of virus evolution using ancestral recombination graphs
Ancestral recombination graphs (ARGs) represent the coalescent history at every locus in the genome, subject to mutation and recombination. Modelling viruses using the coalescent with recombination remains less explored, due to the potential for complex, multi-scale dynamics. Using perturbative methods, we establish mathematical bounds that describe the balance between inter-host and within-host…
Ancestral recombination graphs (ARGs) are a tool used to study the coalescent history of genomic locations in viruses, accounting for mutation and recombination events. However, modeling viruses with ARGs has not been extensively explored due to the complexity of their dynamics. Researchers have now developed a method to analyze viruses using ARGs, focusing on the Epstein-Barr Virus (EBV) as a case study.
They found that EBV coalesces predominantly at the inter-host level rather than within the host, which is an important insight into how the virus spreads. To adapt an existing tool called SINGER, originally designed for diploid genetics, the researchers improved its capabilities to handle haploid ARG inference with variable recombination rates.
This adaptation allows for a more nuanced understanding of the intricate relationships between genetic variations and viral evolution. The study sheds light on an apparent paradox: despite frequent recombination, the virus exhibits slow decay of linkage disequilibrium, suggesting that ARG-based methods can separate epistatic effects from shared ancestry.
Furthermore, the researchers demonstrated that ARG-based approaches can recover gene-level selective structure, providing a valuable approach for analyzing virus phylodynamics, especially when dealing with limited data.
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