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Fractional coalescent.

Somayeh Mashayekhi1, Peter Beerli2

  • 1Department of Scientific Computing, Florida State University, Tallahassee, FL 32306 smashayekhi@fsu.edu.

Proceedings of the National Academy of Sciences of the United States of America
|March 15, 2019
PubMed
Summary
This summary is machine-generated.

A new fractional coalescent (f-coalescent) model accounts for offspring variability using parameter α. This model improves population genetic inference and detects environmental heterogeneity, outperforming the standard n-coalescent model.

Keywords:
Bayesian inferencecoalescentenvironmental heterogeneityfractional calculuspopulation genetics

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Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Computational Biology

Background:

  • The standard Kingman's n-coalescent model assumes constant offspring variance.
  • Deviations from this assumption can arise due to environmental factors affecting population dynamics.
  • A more flexible coalescent framework is needed to capture these variations.

Purpose of the Study:

  • Introduce the fractional coalescent (f-coalescent) model.
  • Derive key population genetic parameters under this new model.
  • Assess the f-coalescent's performance in model inference and detecting population heterogeneity.

Main Methods:

  • The f-coalescent is derived from the discrete-time Cannings population model with a variable offspring variance parameter (α).
  • Mathematical derivations for the time to the most recent common ancestor and gene descent probabilities were performed.
  • The f-coalescent was implemented in the Migrate software, and simulation studies were conducted.

Main Results:

  • The f-coalescent parameter α influences the distribution of waiting times, with specific values increasing short intervals and allowing occasional long ones.
  • When α=1, the f-coalescent is equivalent to the Kingman's n-coalescent.
  • Simulation studies demonstrated accurate estimation of α values and improved model fit compared to the n-coalescent for real data (H1N1, malaria).

Conclusions:

  • The fractional coalescent (f-coalescent) provides a more flexible framework for population genetic modeling.
  • The f-coalescent can detect environmental heterogeneity and offers improved model fit over the n-coalescent.
  • Its implementation in Migrate facilitates testing for deviations from standard coalescent assumptions.