2025/03/23 by Graham Gower, Nathaniel S. Pope, Murillo F. Rodrigues +28 · 1 voice · 7 citations
Biochemistry, Genetics and Molecular Biology · #Artificial intelligence #Background selection #Benchmark (surveying) #Benchmarking #Biology #Computational biology #Computer science #Evolution and Genetic Dynamics #Gene #Genetic and phenotypic traits in livestock #Genetic diversity and population structure #Genetics #Genome #Geography #Inference #Machine learning #Population #Robustness (evolution) #Selection (genetic algorithm)
paper · pdf · doi:10.1101/2025.03.23.644823
published in bioRxiv (Cold Spring Harbor Laboratory) (Cold Spring Harbor Laboratory)
openalex publication_date 2025/03/23 · openalex created_date 2025/03/24 · openalex updated_date 2026/07/28
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.