Human Ancestries Simulation and Inference: a Review of Ancestral Recombination Graph-Based Approaches

TL;DR

This review comprehensively examines ancestral recombination graph (ARG) simulation and inference software, highlighting their performance, usability, and biological realism to guide researchers in developing coalescent-with-recombination samplers.

Contribution

It provides a detailed overview of ARG software developed over three decades, focusing on scalability, flexibility, and biological accuracy, with compiled resources for researchers.

Findings

Many ARG tools have improved scalability and usability.

Challenges in ancestry inference remain significant.

The review offers a curated list of software and resources.

Abstract

There is little debate about the importance of the ancestral recombination graph in population genetics. An important theoretical tool, the main obstacle to its widespread usage is the computational cost required to match the ever-increasing scale of the data being analyzed. Many of these difficulties have been overcome in the past two decades, which have consequently seen the development of increasingly sophisticated ARG simulation and inference software. Nonetheless, challenges remain, especially in the area of ancestry inference. This paper is a comprehensive review of ARG samplers that have emerged in the past three decades to meet the need for scalable and flexible ancestry simulation and inference solutions. It specifically focuses on their performance, usability, and the biological realism of the underlying algorithm, and aims primarily to provide a technical overview of the field for researchers seeking to write their own coalescent-with-recombination sampler. As a complement to this article, we have compiled links to software, source code and documentation and made them available at https://patrickfournier.ca/arg-software-review/graph/.