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Sequential Markov coalescent algorithms for population models with demographic structure.

A Eriksson1, B Mahjani, B Mehlig

  • 1Department of Physics, University of Gothenburg, SE-41296 Gothenburg, Sweden.

Theoretical Population Biology
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PubMed
Summary
This summary is machine-generated.

The sequential Markov coalescent method accurately models population genetics, but may underestimate linkage correlations in populations with reduced gene flow. This study analyzes its performance in various demographic models.

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Area of Science:

  • Population genetics
  • Computational biology
  • Evolutionary genetics

Background:

  • The coalescent theory models genetic drift and population history.
  • Approximations are needed for complex demographic scenarios.
  • Sequential Markov coalescent (SMC) is a computational method for coalescent modeling.

Purpose of the Study:

  • To evaluate the accuracy of the sequential Markov coalescent (SMC) method.
  • To analyze SMC performance across different population structures (bottleneck, divergence, migration).
  • To investigate the correlation between ancestral recombination and linkage disequilibrium.

Main Methods:

  • Analysis of sequential Markov coalescent algorithms.
  • Computation of correlations between common ancestor times and linkage probabilities.
  • Modeling of bottleneck, population divergence, and two-island migration scenarios.

Main Results:

  • SMC generally approximates coalescent processes well in structured populations.
  • An exception was observed in populations with reduced gene flow (low migration).
  • In low-gene-flow scenarios, SMC significantly underestimated linkage correlations.

Conclusions:

  • The sequential Markov coalescent method is a robust tool for population genetics modeling.
  • Care must be taken when applying SMC to populations with restricted gene flow.
  • Further research is needed to understand and correct underestimation in specific demographic contexts.