Brandt, Débora YC; Huber, Christian D; Chiang, Charleston WK; Ortega-Del Vecchyo, Diego; (2024) The Promise of Inferring the Past using the Ancestral Recombination Graph (ARG). Genome Biology and Evolution , 16 (2) , Article evae005. 10.1093/gbe/evae005.

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Abstract

The Ancestral Recombination Graph (ARG) is a structure that represents the history of coalescent and recombination events connecting a set of sequences (Hudson 1991). The full ARG can be represented as a set of genealogical trees at every locus in the genome, annotated with recombination events that change the topology of the trees between adjacent loci and the mutations that occurred along the branches of those trees (Griffiths & Marjoram 1997). Valuable insights can be gained into past evolutionary processes, such as demographic events or the influence of natural selection, by studying the ARG. It is regarded as the “holy grail” of population genetics (Hubisz & Siepel 2020) since it encodes the processes that generate all patterns of allelic and haplotypic variation from which all commonly used summary statistics in population genetic research (e.g. heterozygosity, linkage disequilibrium, etc.) can be derived. Many previous evolutionary inferences relied on summary statistics extracted from the genotype matrix. Evolutionary inferences using the ARG represent a significant advancement as the ARG is a representation of the evolutionary history of a sample that shows the past history of recombination, coalescent and mutation events across a particular sequence. This representation in theory contains as much information, if not more, than the combination of all independent summary statistics that could be derived from the genotype matrix. Consistent with this idea, some of the first ARG-based analyses have allowed more powerful analysis than summary statistic-based analyses (Stern et al. 2019; Speidel et al. 2019; Hubisz et al. 2020; Hejase et al. 2022; Fan et al. 2022, 2023; Link et al. 2023; Zhang et al. 2023). As such, there has been significant interest in the field to investigate two main problems related to the ARG: 1) How can we estimate the ARG based on genomic data, and 2) How can we extract information of past evolutionary processes from the ARG? In this perspective we highlight three topics that pertain to these main issues: The development of computational innovations that enable the estimation of the ARG; remaining challenges in estimating the ARG; and methodological advances for deducing evolutionary forces and mechanisms using the ARG. This perspective serves to introduce the readers to the types of questions that can be explored using the ARG, and to highlight some of the most pressing issues that must be addressed in order to make ARG-based inference an indispensable tool for evolutionary research.

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