Accessible, Realistic Genome Simulation with Selection Using <tt>stdpopsim</tt>

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here, we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsim's core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

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Publication Details

Journal
Molecular Biology and Evolution
Published
2025-09-25
DOI
https://doi.org/10.1093/molbev/msaf236
Citations
4
Primary Topic
Genetic diversity and population structure
Type
article
Field-Weighted Citation Impact
7.27

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Accessible, Realistic Genome Simulation with Selection Using <tt>stdpopsim</tt>

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4 citations
Molecular Biology and Evolution
Genetic diversity and population structure
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article

Accessible, Realistic Genome Simulation with Selection Using <tt>stdpopsim</tt>

Aaron P. Ragsdale, Yan Wong, Jerome Kelleher, Benjamin C. Haller, Andrew D. Kern, Georgia Tsambos, Ilan Gronau, Christian D. Huber, Peter L. Ralph, Jiseon Min, Ornob Alam, Gregor Gorjanc, Graham Gower, Christopher C. Kyriazis, Silas Tittes, Kevin Korfmann, Inés Rebollo, Ryan N. Gutenkunst, Ben Jeffery, Chris C. R. Smith, Kirk E. Lohmueller, Scott T. Small, Daniel R. Schrider, Murillo F. Rodrigues, Nathaniel S. Pope, Maria Izabel A. Cavassim, Xin Huang, Peter D. Fields, Clara T. Rehmann, Linh Tran, Yu Zhang
article en
4 citations

Abstract

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here, we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsim's core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

Molecular Biology and EvolutionVol. 42(11)
Oregon National Primate Research Center (US), University of North Carolina at Chapel Hill (US), University of Copenhagen (DK), University of Vienna (AT), University of Minnesota (US), Pennsylvania State University (US), Roslin Institute (GB), University of Wisconsin–Madison (US), University of Arizona (US), Reichman University (IL), University of California, Los Angeles (US), University of Oregon (US), University of Washington (US), Cornell University (US), University of Oxford (GB), Institute of Human Sciences (AT), Jackson Laboratory (US), Institut de Biologia Evolutiva (ES), New York Genome Center (US), University of Edinburgh (GB)
China Scholarship Council, National Institutes of Health, Biotechnology and Biological Sciences Research Council
Openalex Percentile: Top 3%
Genetic diversity and population structure
7.27
DOIOpen Access PDFOpenAlex
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