Recoverability of Ancestral Recombination Graph Topologies

TL;DR

This paper analyzes the theoretical limits of reconstructing ancestral recombination graph topologies, showing that under realistic parameters like those for SARS-CoV-2, accurate reconstruction is inherently challenging due to mutation and sample size constraints.

Contribution

It provides a probabilistic analysis of ARG topology recoverability under the coalescent model, highlighting the impact of mutation rates and sample sizes on reconstruction accuracy.

Findings

Reconstruction probability decreases with lower mutation rates.

Realistic parameters for SARS-CoV-2 lead to low reconstruction accuracy.

Sample size and mutation rate critically influence ARG recoverability.

Abstract

Recombination is a powerful evolutionary process that shapes the genetic diversity observed in the populations of many species. Reconstructing genealogies in the presence of recombination from sequencing data is a very challenging problem, as this relies on mutations having occurred on the correct lineages in order to detect the recombination and resolve the placement of edges in the local trees. We investigate the probability of recovering the true topology of ancestral recombination graphs (ARGs)under the coalescent with recombination and gene conversion. We explore how sample size and mutation rate affect the inherent uncertainty in reconstructed ARGs; this sheds light on the theoretical limitations of ARG reconstruction methods. We illustrate our results using estimates of evolutionary rates for several biological organisms; in particular, we find that for parameter values that are realistic for SARS-CoV-2, the probability of reconstructing genealogies that are close to the truth is low.