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Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
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Unified framework to evaluate panmixia and migration direction among multiple sampling locations.

Peter Beerli1, Michal Palczewski

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

Genetics
|February 24, 2010
PubMed
Summary

This study introduces a framework for comparing population genetic models using marginal likelihoods. Thermodynamic integration provides more accurate model comparisons than harmonic mean estimators, especially with complex population structures.

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Genetic sampling across geographic locations often misrepresents true population structure.
  • Accurate inference of population genetic models is crucial for understanding evolutionary processes.

Purpose of the Study:

  • To present a framework for comparing and ordering structured population genetic models using marginal likelihoods.
  • To evaluate the accuracy of different marginal likelihood approximation methods in Markov chain Monte Carlo (MCMC) inferences.

Main Methods:

  • Utilized marginal likelihoods to compare structured population models, including panmixia and gene flow models.
  • Compared modified thermodynamic integration and a stabilized harmonic mean estimator for calculating marginal likelihoods.
  • Assessed the influence of prior distributions on model selection outcomes.

Main Results:

  • Modified thermodynamic integration yielded more accurate marginal likelihood estimates than the harmonic mean estimator, particularly with finite MCMC run lengths.
  • Thermodynamic integration demonstrated robustness to prior distribution choices, preserving model order.
  • The harmonic mean estimator showed sensitivity to prior distributions, leading to altered model rankings.

Conclusions:

  • Marginal likelihood approximation via thermodynamic integration in MIGRATE software enables robust evaluation of complex population genetic models.
  • This approach facilitates distinguishing between simple (panmictic) and complex (structured) population scenarios.
  • Accurate model comparison is essential for advancing population genetic research and understanding gene flow dynamics.