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Perfect simulation from population genetic models with selection.

P Fearnhead1

  • 1Department of Statistics, University of Oxford, Oxford, United Kingdom.

Theoretical Population Biology
|September 19, 2001
PubMed
Summary

This study introduces a new method for simulating population genetic samples using the ancestral selection graph (ASG). The novel approach efficiently simulates samples from K-allele models, overcoming previous computational limitations with selection.

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Area of Science:

  • Population genetics
  • Computational biology
  • Evolutionary genetics

Background:

  • Simulating samples from population genetic models with selection using the ancestral selection graph (ASG) is computationally intensive.
  • Current methods face limitations due to exponential increases in computational requirements with selection rate and the need to simulate a single sample from equilibrium.

Purpose of the Study:

  • To develop a more efficient algorithm for simulating samples from population genetic models with selection using the ASG.
  • To overcome the limitations of existing ASG simulation methods, particularly for K-allele models.

Main Methods:

  • Applying the 'coupling from the past' technique to the ancestral selection graph (ASG).
  • Developing a simulation algorithm that does not require prior knowledge of the distribution of a single sample from the population at equilibrium.

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Main Results:

  • The proposed method allows for the simulation of samples from a general K-allele model without needing the distribution of a single sample.
  • Computational requirements for generating samples are reduced compared to simulating the ASG until its ultimate ancestor.
  • For genic selection with parent-independent mutations, computational cost scales quadratically with selection rate, a significant improvement over exponential scaling.

Conclusions:

  • The 'coupling from the past' approach provides an efficient method for simulating samples from ASGs in population genetics.
  • This algorithm advances the simulation of genetic diversity under selection, with practical applications demonstrated at a microsatellite locus.