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Maximum likelihood estimation of recombination rates from population data.

M K Kuhner1, J Yamato, J Felsenstein

  • 1Department of Genetics, University of Washington, Seattle, Washington 98195, USA. mkkhuner@genetics.washington.edu

Genetics
|November 7, 2000
PubMed
Summary

This study introduces a new method for estimating genetic recombination (r) and effective population size (Theta) from molecular data. The Metropolis-Hastings technique provides accurate Theta estimates but requires long sequences for precise r values, especially at low rates.

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Area of Science:

  • Population Genetics
  • Molecular Evolution
  • Bioinformatics

Background:

  • Estimating population genetic parameters like recombination rate (r) and effective population size (Theta) is crucial for understanding evolutionary processes.
  • Accurate inference of these parameters from molecular data is essential for population genetics studies.

Purpose of the Study:

  • To develop and present a novel computational method for the co-estimation of the per-site recombination rate (r) and effective population size (Theta) from population samples of molecular data.
  • To assess the accuracy and potential biases of the proposed estimation method across various parameter ranges.

Main Methods:

  • Utilized Metropolis-Hastings sampling to explore a vast number of recombinant genealogies.
  • Weighted genealogical reconstructions by their posterior probability relative to the observed molecular data and parameter values.

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  • The method accommodates known, externally derived differences in recombination rates across locations but does not estimate these differences intrinsically.

    • Main Results:

    • The developed method yields accurate and apparently unbiased estimates for Theta across a broad spectrum of parameter values.
    • Accurate estimation of r requires very long sequences, particularly when both Theta and r are low.
    • Under low Theta and r conditions, estimates of r tend to be upwardly biased.

    Conclusions:

    • The Metropolis-Hastings approach offers a robust method for co-estimating key population genetic parameters.
    • The study highlights the sequence length requirements for accurate recombination rate estimation, especially in scenarios with low genetic variation and recombination.
    • The method was successfully applied to analyze molecular data from the human lipoprotein lipase locus.