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Computing coalescence rates for complex demographies and sampling configurations.

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  • 1Department of Statistics, University of Michigan.

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Summary
This summary is machine-generated.

This study introduces demestats, a new software library for analyzing population history using genetic data. It improves the accuracy of inferring recent demographic changes, like population size and migration, compared to older methods.

Keywords:
demographic inferencepopulation genetics

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Area of Science:

  • Population genetics
  • Computational biology
  • Bioinformatics

Background:

  • Population history inference relies on coalescence time distributions.
  • Pairwise coalescent rates are popular but limited for recent history.
  • Recent coalescences are rare in small sample sizes.

Purpose of the Study:

  • Introduce demestats, a software library for computing first-coalescence and cross-coalescence rates.
  • Develop statistics with improved power to resolve recent demographic events.
  • Enable differentiable demographic models for parameter inference.

Main Methods:

  • Compute hazard of first coalescent event for structured demographic models (demes format).
  • Combine exact calculations with mean-field approximations for scalability.
  • Implement differentiable statistics for parameter optimization.

Main Results:

  • Simulations show demestats statistics outperform pairwise summaries for recent population size change and migration.
  • Analysis of 1000 Genomes Project data provides new insights into human population expansion rates.
  • Successfully applied to tree sequences for demographic inference.

Conclusions:

  • Demestats offers a powerful new approach for inferring recent population history from genetic data.
  • The library enhances the resolution of demographic inference, particularly for recent events.
  • Provides valuable insights into human population dynamics and expansion.