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Related Experiment Videos

Estimating effective population size from samples of sequences: a bootstrap Monte Carlo integration method.

J Felsenstein1

  • 1Department of Genetics, University of Washington, Seattle 98195.

Genetical Research
|December 1, 1992
PubMed
Summary

This study introduces a Monte Carlo integration method to estimate effective population size (N(e)) and related parameters. The approach approximates likelihood calculations by analyzing bootstrap samples of nucleotide sequences, making complex population genetics inferences more accessible.

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Estimating effective population size (N(e)) is crucial for understanding evolutionary dynamics.
  • Traditional maximum likelihood methods for N(e) estimation involve summing over all possible genealogies, a computationally intensive task.
  • Existing methods struggle with the complexity of integrating over diverse tree topologies and branch lengths.

Purpose of the Study:

  • To develop a practical method for estimating population genetics parameters like N(e) using maximum likelihood.
  • To overcome the computational challenges of summing over all possible genealogies in sequence data analysis.
  • To approximate likelihood calculations for nucleotide sequences from random-mating populations.

Main Methods:

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  • Utilizes Monte Carlo integration to approximate likelihood calculations.
  • Employs bootstrap sampling of sites to generate multiple data sets.
  • Estimates a maximum likelihood tree for each bootstrap sample.
  • Assumes resulting trees approximate the distribution of the full data's likelihood surface.

    • Main Results:

    • The proposed method provides an approximate evaluation of likelihoods for estimating N(e) or 4N(e)μ.
    • Maximum likelihood estimates for population genetics parameters can be derived from the estimated likelihood curve.
    • The computational effort is approximately 100 times that of standard phylogenetic estimation but remains practical.

    Conclusions:

    • A novel Monte Carlo integration approach enables approximate maximum likelihood estimation of effective population size.
    • This method offers a practical solution for complex likelihood computations in population genetics.
    • The current method does not account for genetic recombination.