Abstract
We present a novel algorithm, implemented in the software ARGinfer, for probabilistic inference of the Ancestral Recombination Graph under the Coalescent with Recombination. Our Markov Chain Monte Carlo algorithm takes advantage of the Succinct Tree Sequence data structure that has allowed great advances in simulation and point estimation, but not yet probabilistic inference. Unlike previous methods, which employ the Sequentially Markov Coalescent approximation, ARGinfer uses the Coalescent with Recombination, allowing more accurate inference of key evolutionary parameters. We show using simulations that ARGinfer can accurately estimate many properties of the evolutionary history of the sample, including the topology and branch lengths of the genealogical tree at each sequence site, and the times and locations of mutation and recombination events. ARGinfer approximates posterior probability distributions for these and other quantities, providing interpretable assessments of uncertainty that we show to be well calibrated. ARGinfer is currently limited to tens of DNA sequences of several hundreds of kilobases, but has scope for further computational improvements to increase its applicability.
Highlights
A core problem of population genetics is to infer the genealogical history of a sample of homologous DNA sequences, including the recombination, mutation and branching events that produced the currently-observed sample
One of the important challenges in population genetics is to reconstruct the historical mutation, recombination and shared ancestor events that underly a sample of DNA sequences drawn from a population
Aspects of this history can inform us about evolutionary processes, ages of mutations and times of common ancestors, and historical population sizes and migration rates
Summary
A core problem of population genetics is to infer the genealogical history of a sample of homologous DNA sequences, including the recombination, mutation and branching events that produced the currently-observed sample. The Coalescent with Recombination (CwR) [1] provides a simple yet powerful prior distribution for the genealogical history of a set of sequences. The sequence data can be poorly informative about some parameters, so that multiple topologically-different ARGs have similar likelihoods. For these reasons, only limited progress has been made in the ARG inference problem, resulting in little use of ARG-based inference in population genetics. Inference is often based on summary statistics, leading to both information loss and lack of the quantification of uncertainty that model-based probabilistic inference offers
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