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Bayesian inference of ancestral recombination graphs.

Ali Mahmoudi1, Jere Koskela2, Jerome Kelleher3

  • 1Melbourne Integrative Genomics / School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.

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Summary

We developed ARGinfer, a new algorithm for inferring the Ancestral Recombination Graph (ARG) using the Coalescent with Recombination model. This method accurately estimates evolutionary history, including mutation and recombination events, providing reliable uncertainty assessments.

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Area of Science:

  • Computational Biology
  • Population Genetics
  • Bioinformatics

Background:

  • Probabilistic inference of the Ancestral Recombination Graph (ARG) is crucial for understanding evolutionary processes.
  • Existing methods often rely on approximations like the Sequentially Markovian Coalescent (SMC'), limiting accuracy.
  • The Succinct Tree Sequence data structure has advanced simulation and point estimation but not probabilistic inference.

Purpose of the Study:

  • To introduce ARGinfer, a novel algorithm for probabilistic ARG inference under the Coalescent with Recombination (CWR).
  • To leverage the Succinct Tree Sequence data structure for accurate evolutionary parameter estimation.
  • To provide well-calibrated uncertainty assessments for inferred evolutionary histories.

Main Methods:

  • Implementation of a novel Markov Chain Monte Carlo (MCMC) algorithm within the ARGinfer software.
  • Utilizing the Succinct Tree Sequence data structure for efficient computation.
  • Employing the Coalescent with Recombination (CWR) model, avoiding SMC' approximations.

Main Results:

  • ARGinfer accurately estimates key properties of evolutionary history, including genealogical tree topology and branch lengths.
  • The algorithm precisely identifies the times and locations of mutation and recombination events.
  • ARGinfer provides well-calibrated, interpretable posterior probability distributions and uncertainty assessments.

Conclusions:

  • ARGinfer offers a more accurate approach to ARG inference compared to SMC'-based methods.
  • The software enables detailed reconstruction of evolutionary histories from sequence data.
  • Future computational improvements will expand ARGinfer's applicability to larger datasets.