AI Insight
This article presents a comprehensive review of computational methods for simulating and inferring Ancestral Recombination Graphs (ARGs), which are theoretical structures used in population genetics to model the shared ancestry and recombination history of genomic sequences. The authors survey ARG-based software developed over the past three decades, evaluating tools based on their performance, usability, and the degree of biological realism incorporated into their underlying algorithms. The review identifies significant progress in scalability over the past two decades while acknowledging that challenges persist, particularly in the accurate inference of ancestry from large genomic datasets.
Why it matters
Improved ARG inference methods have direct implications for understanding human evolutionary history, detecting natural selection, and advancing medical genomics by better characterizing the genetic relationships between populations. A consolidated, technically oriented overview of available tools lowers the barrier for researchers developing new computational methods in population genetics.
arXiv:2601.09634v3 Announce Type: replace
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/.