Effects of linked selective sweeps on demographic inference and model selection

Abstract

The availability of large-scale population genomic sequence data has resulted in an explosion in efforts to infer the demographic histories of natural populations across a broad range of organisms. As demographic events alter coalescent genealogies they leave detectable signatures in patterns of genetic variation within and between populations. Accordingly, a variety of approaches have been designed to leverage population genetic data to uncover the footprints of demographic change in the genome, thereby elucidating population histories. The vast majority of these methods make the simplifying assumption that the measures of genetic variation used as their input are unaffected by natural selection. However, natural selection can dramatically skew patterns of variation not only at selected sites, but at linked, neutral loci as well. Here we assess the impact of recent positive selection on demographic inference by characterizing the performance of three popular methods through extensive simulation of datasets with varying numbers of linked selective sweeps. In particular, we examined three different demographic models relevant to a number of species, finding that positive selection can bias parameter estimates of each of these models-often severely. Moreover, we find that selection can lead to incorrect inferences of population size changes when none have occurred. We argue that the amount of recent positive selection required to skew inferences may often be acting in natural populations. These results suggest that demographic studies conducted in many species to date may have exaggerated the extent and frequency of population size changes.3

Estimating the parameters of background selection and selective sweeps in Drosophila in the presence of gene conversion

“…There is increasing evidence that this is also true for many other organisms (1,3). Such processes have important implications for attempts to estimate demographic parameters, which usually ignore these complications, as has been pointed out before (53)(54)(55)(56). This is especially important when selection at linked sites distorts gene genealogies and hence site frequency spectra, because these are the main basis for inferring demographic parameters.…”

Evolution of sociality in spiders leads to depleted genomic diversity at both population and species levels

“…Models including migration provide the best fit for all species except for S. pacificus , based on Akaike information criterion. We note that the estimated parameters of demographic modelling may be biased due to selection on the markers used or on linked loci (Schrider, Shanku, & Kern, ). The extent of such bias depends on the combination of the strength and efficacy of selection (effective population size and the selection coefficient, N e s) and the extent of linkage disequilibrium.…”

A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data

“…If a subset of the genome is chosen, coding DNA should not constitute the majority of the data set because of the confounding effects of selection on demographic inference. Excluding sequences that are close to coding regions or that have low recombination rates can also help to mitigate the confounding impacts of selection (Schrider et al, ).…”