Year: 2025
Type: article
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.
Source: Molecular Biology and Evolution
Authors: Graham Gower, Nathaniel S Pope, Murillo F Rodrigues, Silas Tittes, Linh N Tran, Ornob Alam, Maria Izabel A Cavassim, Peter D Fields, Benjamin C Haller, Xin Huang, Ben Jeffrey, Kevin Korfmann, Christopher C Kyriazis, Jiseon Min, Inés Rebollo, Clara T Rehmann, Scott T Small, Chris C R Smith, Georgia Tsambos, Yan Wong, Yu Zhang, Christian D Huber, Gregor Gorjanc, Aaron P Ragsdale, Ilan Gronau, Ryan N Gutenkunst, Jerome Kelleher, Kirk E Lohmueller, Daniel R Schrider, Peter L Ralph, Andrew D Kern
Institutions: University of Copenhagen, University of Oregon, Oregon Health & Science University, University of Arizona, New York University, University of California, Los Angeles, Jackson Laboratory, Cornell University, University of Vienna, University of Oxford, University of Minnesota, University of Washington, Roslin Institute, University of Edinburgh, Pennsylvania State University, University of Wisconsin–Madison, Reichman University, University of North Carolina at Chapel Hill
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,418
Awards: Genes & Traits for Healthy Animals: Understanding the origins of animal phenotypes, Genes & Traits for Healthy Animals: Identifying the genomic basis of complex traits in farmed animals, Joint inferences of natural selection between sites and populations, Development of a high-throughput pipeline to identify causal variants and its demonstration in pig muscle, Computational Population Genetics, The role of admixture in human evolution, Scaling up computational genomics with tree sequences, Deep learning for population genetics, Population genomics of the selective effects of new mutations, Advancing evolutionary genetics through deep learning, Population genomic inferences of history and selection across populations and time
OpenAlex ID: W4414487805