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Maximum likelihood estimators for scaled mutation rates in an equilibrium mutation-drift model.

Claus Vogl1, Lynette C Mikula2, Conrad J Burden3

  • 1Department of Biomedical Sciences, Vetmeduni Vienna, Veterinärplatz 1, A-1210 Wien, Austria.

Theoretical Population Biology
|June 21, 2020
PubMed
Summary

This study refines mutation rate estimation in population genetics using maximum likelihood. It corrects previous errors and applies new methods to Drosophila melanogaster mutation data.

Keywords:
Decoupled Moran diffusionMutation–drift modelScaled mutation parametersStrand-symmetryWright–Fisher diffusion

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Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Neutral mutations are key to understanding genetic drift.
  • Estimating mutation rates is crucial for evolutionary studies.
  • Previous methods had limitations in accuracy and scope.

Purpose of the Study:

  • To develop accurate maximum likelihood estimators for scaled mutation rates.
  • To address errors in prior estimations for general and strand-symmetric models.
  • To apply these refined methods to real biological data.

Main Methods:

  • Utilizing the stationary sampling distribution of neutral Moran or Wright-Fisher diffusion.
  • Deriving estimators for general, reversible, and strand-symmetric mutation rate matrices.
  • Analyzing site frequency spectrum data from a population sample.

Main Results:

  • New maximum likelihood estimators for scaled mutation rates were derived.
  • An error in previous work (Burden and Tang, 2017) was identified and corrected.
  • The method was successfully applied to Drosophila melanogaster autosomal intron data.

Conclusions:

  • The refined methods provide more accurate mutation rate estimates.
  • This work improves the analysis of genetic variation data.
  • The findings contribute to a better understanding of evolutionary processes.