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An efficient coalescent model for heterochronously sampled molecular data.

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Summary

This study introduces a new method using the Tajima coalescent to model longitudinal samples for inferring population size dynamics. This approach improves scalability and accuracy for evolutionary history analysis, especially with ancient DNA.

Keywords:
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Area of Science:

  • Evolutionary biology
  • Population genetics
  • Bioinformatics

Background:

  • Molecular sequence variation reveals evolutionary history and population size dynamics.
  • The Kingman coalescent model is standard but does not scale well with increasing sample sizes.
  • Longitudinal sampling is crucial for applications like ancient DNA and pathogen evolution studies.

Purpose of the Study:

  • To develop a scalable inference scheme for effective population size trajectories using the Tajima coalescent model.
  • To model longitudinal samples by considering individuals labeled by sampling time, not just identity.
  • To enhance statistical power through variance reduction and improved parameter identifiability.

Main Methods:

  • Utilized the Tajima coalescent process and the infinite-sites mutation model for longitudinal data.
  • Developed an efficient algorithm for likelihood calculation.
  • Employed a Bayesian nonparametric procedure and a novel MCMC sampler for inferring population size trajectories and model parameters.

Main Results:

  • Proposed an efficient inference scheme for reconstructing effective population size trajectories from longitudinal samples.
  • Demonstrated the utility of the Tajima coalescent for modeling ancestry based on sampling time.
  • Successfully compared the new procedure with existing state-of-the-art methods via simulations and ancient bison DNA analysis.

Conclusions:

  • The proposed method offers a scalable and statistically robust approach for analyzing population size dynamics from longitudinal molecular sequence data.
  • This work advances the modeling of evolutionary processes, particularly for ancient DNA and rapidly evolving pathogens.
  • The developed MCMC sampler provides a powerful tool for exploring complex genealogical models and parameters.