KwARG: Parsimonious reconstruction of ancestral recombination graphs with recurrent mutation

TL;DR

KwARG is a new heuristic algorithm that reconstructs ancestral recombination graphs with minimal recombination and mutation events, providing plausible evolutionary histories from genetic data.

Contribution

It introduces a parsimony-based greedy heuristic for reconstructing ancestral recombination graphs considering recurrent mutations.

Findings

Performs well compared to existing methods

Outputs multiple candidate solutions with controllable event proportions

Available as open-source software on GitHub

Abstract

The reconstruction of possible histories given a sample of genetic data in the presence of recombination and recurrent mutation is a challenging problem, but can provide key insights into the evolution of a population. We present KwARG, which implements a parsimony-based greedy heuristic algorithm for finding plausible genealogical histories (ancestral recombination graphs) that are minimal or near-minimal in the number of posited recombination and mutation events. Given an input dataset of aligned sequences, KwARG outputs a list of possible candidate solutions, each comprising a list of mutation and recombination events that could have generated the dataset; the relative proportion of recombinations and recurrent mutations in a solution can be controlled via specifying a set of 'cost' parameters. We demonstrate that the algorithm performs well when compared against existing methods. The software is made available on GitHub.