A model for background selection in nonequilibrium populations
Year: 2026
Type: article
Abstract: In many taxa, levels of genetic diversity vary along their genomes. The framework of background selection models this variation in terms of linkage to constrained sites, and recent applications have been able to explain a large portion of the variation in human genomes. However, these studies have also yielded conflicting results, stemming from two key limitations. First, existing models are inaccurate in a critical region of parameter space (Nes∼-1), where the local reduction in diversity is sharpest. Second, they assume a constant population size over time. Here, we develop predictions for diversity under background selection based on the Hill-Robertson system of two-locus statistics, which allows for population size changes. We treat the joint effect of multiple selected loci independently, but we show that interference among them is well captured through local rescaling of mutation, recombination, and selection in an iterative procedure that converges quickly. We further accommodate existing background selection theory to nonequilibrium demography, bridging the gap between weak and strong selection. Simulations show that our predictions are accurate across the entire range of selection coefficients. We characterize the temporal dynamics of linked selection under population size changes and demonstrate that patterns of diversity can be misinterpreted by other models. Specifically, biases due to the incorrect assumption of equilibrium carry over to downstream inferences of the distribution of fitness effects and deleterious mutation rate. Jointly, modeling demography and linked selection therefore improves our understanding of the genomic landscape of diversity, which will help refine inferences of linked selection in humans and other species.
Source: Genetics
Authors: Gustavo V Barroso, Aaron P Ragsdale
Institution: University of Wisconsin–Madison
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funder: National Institutes of Health
OpenAlex ID: W7140185767