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Estimating effective population size changes from preferentially sampled genetic sequences.

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This study enhances methods for estimating population size changes using molecular data by jointly modeling genetic lineages and sampling times. The improved approach incorporates time-varying factors for more accurate population dynamics analysis.

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Statistical Modeling

Background:

  • Estimating effective population size (Ne) fluctuations is crucial for understanding population dynamics.
  • Coalescent theory and statistical models are standard tools for inferring Ne from molecular sequences.
  • Previous methods often assumed known genealogies and simplified sampling time distributions.

Purpose of the Study:

  • To develop an improved statistical framework for estimating effective population size trajectories.
  • To integrate joint Bayesian estimation of genealogies and sampling times.
  • To enhance population size estimation by incorporating time-varying covariates into sampling time models.

Main Methods:

  • Developed a Bayesian framework for joint estimation of genealogy and effective population size.
  • Extended the sampling time model to include time-varying covariates.
  • Validated the new methodology through simulation studies.

Main Results:

  • The enhanced model provides more accurate estimates of effective population size trajectories.
  • Joint estimation of genealogy and sampling times improves inference, especially when sampling times are informative.
  • The framework successfully analyzes real-world data from influenza and Ebola virus.

Conclusions:

  • The proposed methodology offers a more robust approach to inferring population dynamics from molecular sequence data.
  • Incorporating informative sampling times and covariates significantly improves the estimation of effective population size.
  • This framework has broad applicability in evolutionary and epidemiological studies.