Background selection and the statistics of population differentiation: consequences for detecting local adaptation

Background selection is a process whereby recurrent deleterious mutations cause a decrease in the effective population size and genetic diversity at linked loci. Several authors have suggested that variation in the intensity of background selection could cause variation in F ST across the genome, which could confound signals of local adaptation in genome scans. We performed realistic simulations of DNA sequences, using parameter estimates from humans and sticklebacks, to investigate how variation in the intensity of background selection affects different statistics of population differentiation. We show that, in populations connected by gene flow, Weir & Cockerham’s (1984) estimator of F ST is largely insensitive to locus-to-locus variation in the intensity of background selection. Unlike F ST , however, d XY is negatively correlated with background selection. We also show that background selection does not greatly affect the false positive rate in F ST outlier studies. Overall, our study indicates that background selection will not greatly interfere with finding the variants responsible for local adaptation.