Accessible, Realistic Genome Simulation with Selection Using stdpopsim

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.