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Estimating effective population size from temporally spaced samples with a novel, efficient maximum-likelihood

Tin-Yu J Hui1, Austin Burt2

  • 1Department of Life Sciences, Silwood Park Campus, Imperial College London, Ascot, Berkshire SL5 7PY, United Kingdom tin-yu.hui11@imperial.ac.uk.

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Summary
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A new method accurately estimates effective population size (Ne) using temporal data. This approach is faster and handles larger populations than previous techniques, aiding evolutionary biology research.

Keywords:
effective population sizegenetic driftmaximum-likelihood estimation

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Area of Science:

  • Population genetics
  • Evolutionary biology
  • Quantitative genetics

Background:

  • Effective population size (Ne) is crucial for understanding genetic drift and evolutionary trajectories.
  • Estimating Ne typically involves analyzing allele frequencies across multiple time points.
  • Existing likelihood methods for Ne estimation are computationally demanding and limited to smaller population sizes.

Purpose of the Study:

  • To develop a novel, computationally efficient likelihood-based estimator for contemporary effective population size (Ne) using temporal allele frequency data.
  • To overcome the limitations of existing methods, particularly their computational intensity and inability to handle large Ne values.
  • To provide a robust tool for estimating Ne in diverse population genetics and evolutionary biology scenarios.

Main Methods:

  • Development of a new likelihood-based estimator utilizing a hidden Markov algorithm.
  • Application of continuous approximations to allele frequencies and transition probabilities for computational efficiency.
  • Extensive simulations to evaluate the performance and accuracy of the proposed estimator.

Main Results:

  • The new estimator demonstrates superior accuracy and lower variance compared to existing methods.
  • Computational time is reduced by at least 1000-fold, enabling rapid Ne estimation.
  • The estimator accommodates effective population sizes up to several million, significantly expanding applicability.
  • The method successfully handles scenarios with non-constant Ne and can be used for likelihood-ratio tests.

Conclusions:

  • The developed estimator offers a significant advancement in accurately and efficiently estimating effective population size from temporal data.
  • This method broadens the scope of population genetic studies by enabling the analysis of larger populations and complex demographic histories.
  • An R package "NB" is available, facilitating the practical implementation of this new Ne estimation technique.