Direct and indirect impacts of positive selection on genomic variation in Drosophila serrata

Understanding the extent to which microevolutionary adaptation relies on novel beneficial mutations, as opposed to previously neutral standing genetic variation, is an important goal of evolutionary genetics. Progress towards this goal has been enhanced during the genomic era through the study of selective sweeps. Selective sweeps fall into two categories: hard sweeps via new mutations and soft sweeps via pre-existing mutations. However, data are currently lacking on the relative frequency of these two types of selective sweep. In this study, we examined 110 whole genome sequences from Drosophila serrata sampled from eastern Australia and searched for hard and soft sweeps using a deep learning algorithm (diploS/HIC). Analyses revealed that approximately 15% of the D. serrata genome was directly impacted by soft sweeps, and that 46% of the genome was indirectly influenced via linkage to these soft sweeps. In contrast, hard sweep signatures were very rare, only accounting for 0.1% of the scanned genome. Gene ontology enrichment analysis further supported our confidence in the accuracy of sweep detection as several traits expected to be under frequent selection due to evolutionary arms races (e.g. immunity and sperm competition) were detected. Within soft sweep regions and those flanking them, there was an over-representation of SNPs with predicted deleterious effects, suggesting positive selection drags deleterious variants to higher frequency due to their linkage with beneficial loci. This study provides insight into the direct and indirect contributions of positive selection in shaping genomic variation in natural populations.