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Accelerated Bayesian inference of population size history from recombining sequence data.

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Population History Learning by Averaging Sampled Histories (PHLASH) infers population history from whole-genome data. This new method is faster, more accurate, and provides uncertainty quantification for population genetics research.

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Area of Science:

  • Population genetics
  • Computational biology
  • Genomic data analysis

Background:

  • Inferring population history is crucial for understanding evolutionary processes.
  • Existing methods for demographic inference from genomic data have limitations in speed and accuracy.
  • Accurate reconstruction of past population events requires robust computational tools.

Purpose of the Study:

  • Introduce Population History Learning by Averaging Sampled Histories (PHLASH), a novel method for inferring population history.
  • Develop an accurate and adaptive estimator for demographic inference using whole-genome sequence data.
  • Provide a computationally efficient tool for population genetics research.

Main Methods:

  • PHLASH utilizes random, low-dimensional projections of the coalescent intensity function.
  • It averages these projections from the posterior distribution of a coalescent-like model.
  • A key advance is a new algorithm for computing the score function of coalescent hidden Markov models.

Main Results:

  • PHLASH demonstrates faster performance and lower error rates compared to SMC++, MSMC2, and FITCOAL on simulated data.
  • The method offers automatic uncertainty quantification.
  • It enables new Bayesian testing procedures for population structure and ancient bottlenecks.

Conclusions:

  • PHLASH is an accurate, adaptive, and computationally efficient method for inferring population history from genomic data.
  • The software package is user-friendly and leverages GPU acceleration.
  • PHLASH advances the field of population genetics by providing improved tools for demographic inference and hypothesis testing.