Leveraging the joint site frequency spectrum to detect genomic regions of early divergence
Detecting genomic loci underlying local adaptation and population divergence is a central goal in population genetics. Such loci have been detected in genomic data using a variety of approaches, but the statistical performance of these approaches can depend substantially on the frequencies of the alleles underlying phenotypic adaptation. In particular, multiple evolutionary modeling studies have shown that rare alleles of large effect can make substantial contributions to phenotypic differentiation in the early stages of adaptation, but most selection inference methods are not sensitive to rare alleles. We used simulations of evolutionary divergence to compare commonly-used FST scans to a likelihood-based approach that interrogates the whole frequency spectrum. We found that the likelihood-based approach outperforms FST when low frequency alleles play a substantial role in driving trait divergence. We applied both approaches to genomic data from Ostrinia nubilalis (the European Corn Borer), a species in which population-specific variation in circannual rhythms and mate preference phenotypes have driven recent reproductive isolation. The likelihood-based approach recovers previously discovered genomic loci and finds several new candidate regions that may be relevant for reproductive isolation between populations. Our findings demonstrate how the two-dimensional frequency spectrum may help to identify loci contributing to reproductive isolation in contexts when commonly used methods are less powerful.