Abstract
A major goal of population genetics has been to determine the extent to which selection at linked sites influences patterns of neutral nucleotide diversity in the genome. Multiple lines of evidence suggest that diversity is influenced by both positive and negative selection. For example, in many species there are troughs in diversity surrounding functional genomic elements, consistent with the action of either background selection (BGS) or selective sweeps. In this study, we investigated the causes of the diversity troughs that are observed in the wild house mouse genome. Using the unfolded site frequency spectrum (uSFS), we estimated the strength and frequencies of deleterious and advantageous mutations occurring in different functional elements in the genome. We then used these estimates to parameterize forward-in-time simulations of chromosomes, using realistic distributions of functional elements and recombination rate variation in order to determine if selection at linked sites can explain the observed patterns of nucleotide diversity. The simulations suggest that BGS alone cannot explain the dips in diversity around either exons or conserved non-coding elements (CNEs). A combination of BGS and selective sweeps, however, can explain the troughs in diversity around CNEs. This is not the case for protein-coding exons, where observed dips in diversity cannot be explained by parameter estimates obtained from the uSFS. We discuss the extent to which our results provide evidence of sweeps playing a role in shaping patterns of nucleotide diversity and the limitations of using the uSFS for obtaining inferences of the frequency and effects of advantageous mutations.
Author Summary We present a study examining the causes of variation in nucleotide diversity across the mouse genome. The status of mice as a model organism in the life sciences makes them an excellent model system for studying molecular evolution in mammals. In our study, we analyse how natural selection acting on new mutations can affect levels of nucleotide diversity through the processes of background selection and selective sweeps. To perform our analyses, we first estimated the rate and strengths of selected mutations from a sample of wild mice and then use our estimates in realistic population genetic simulations. Analysing simulations, we find that both harmful and beneficial mutations are required to explain patterns of nucleotide diversity in regions of the genome close to gene regulatory elements. For protein-coding genes, however, our approach is not able to fully explain observed patterns and we think that this is because there are strongly advantageous mutations that occur in protein-coding genes that we were not able to detect.
