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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Accessible, Realistic Genome Simulation with Selection Using stdpopsim.

Graham Gower1, Nathaniel S Pope2, Murillo F Rodrigues2,3

  • 1Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, Copenhagen, Denmark.

Molecular Biology and Evolution
|September 25, 2025
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Summary
This summary is machine-generated.

This study introduces an enhanced simulation framework, stdpopsim, to model evolutionary selection across diverse genomes. The tool aids in benchmarking methods for detecting selection and understanding its impact on genetic variation.

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Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Computational Biology

Background:

  • Evolutionary selection shapes genetic variation, but realistic simulations are computationally challenging.
  • Existing tools like stdpopsim lack comprehensive modeling of various selection modes.
  • Accurate simulation is crucial for benchmarking methods detecting selection and exploring evolutionary theory.

Purpose of the Study:

  • To extend the stdpopsim library to simulate diverse selection modes, including background selection and selective sweeps.
  • To incorporate species-specific genomic annotations and distributions of fitness effects (DFE) into simulations.
  • To provide a reproducible and accessible framework for population genetics research.

Main Methods:

  • Extended the stdpopsim framework to support simulations of background selection, selective sweeps, and various DFEs.
  • Integrated species-specific genomic annotations and published DFE estimates.
  • Benchmarked demographic inference, DFE estimation, and selective sweep detection methods using the enhanced framework.

Main Results:

  • Demographic inference methods show robustness to selection on linked sites.
  • DFE-inference methods are sensitive to underlying model assumptions.
  • Genomic features like recombination rate and functional density impact the power to detect selective sweeps.

Conclusions:

  • The enhanced stdpopsim framework offers a powerful resource for simulating selection in complex genomic and demographic contexts.
  • It facilitates reproducible research into the interplay of selection with other evolutionary forces.
  • The framework aids in evaluating and improving methods for detecting selection and estimating evolutionary parameters.