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Are Skyline Plot-Based Demographic Estimates Overly Dependent on Smoothing Prior Assumptions?

Kris V Parag1,2, Oliver G Pybus2, Chieh-Hsi Wu3

  • 1MRC Centre for Global Infectious Disease Analysis, Imperial College London, London W2 1PG, UK.

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|May 14, 2021
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Summary

A new statistic, Omega, quantifies how much Bayesian phylogenetic estimates rely on prior assumptions versus coalescent data. This helps detect overconfident population size inferences and improve uncertainty quantification.

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Area of Science:

  • Bayesian phylogenetics
  • Population genetics
  • Evolutionary biology

Background:

  • Coalescent processes are key to inferring population size changes from phylogenetic trees.
  • Current methods (Bayesian Skyline Plot, Skyride, Skygrid) use piecewise-constant models with smoothing priors.
  • These priors can introduce information not present in the data, potentially biasing estimates.

Purpose of the Study:

  • Introduce a novel statistic, Omega (Ω), to quantify the influence of prior assumptions on phylogenetic estimates.
  • Disaggregate contributions of coalescent data and priors to posterior estimate precision.
  • Measure mutual information introduced by smoothing priors.

Main Methods:

  • Developed the Omega (Ω) statistic based on information theory principles.
  • Applied Ω to analyze piecewise-constant population models and common smoothing priors.
  • Evaluated the impact of priors on effective population size (Ne) estimation precision.

Main Results:

  • Common smoothing priors can lead to overconfident and misleading Ne estimates due to model overparametrization.
  • Omega (Ω) effectively quantifies the reliance of estimates on prior assumptions.
  • The statistic highlights cases where Ne estimates are overly influenced by priors, even with good data.

Conclusions:

  • Omega (Ω) is a valuable tool for assessing the reliability of Bayesian phylogenetic estimates.
  • It aids in detecting over-reliance on prior assumptions in effective population size inference.
  • The statistic improves the quantification of uncertainty in phylodynamic analyses.