Detecting signatures of positive selection in non-model species using genomic data

Understanding how natural selection shapes genetic variation in populations is of paramount importance in evolutionary biology. Affordable high-throughput sequencing now allows the generation of genome-wide data for non-model species, thereby stimulating research aimed at determining the genomic basis of adaptation to local environmental conditions. However, although these adaptive loci show characteristic signatures of positive selection, several other processes can lead to similar patterns, rendering the search for outlier loci a challenging task. Given that all these methods rely on different explicit (data requirements) or implicit (underlying population models) assumptions, they have limitations that often remain unknown to non-population geneticists. Simply applying different tests of selection to the generated data can yield unreliable results that include many false positives and negatives, therefore concealing the true evolutionary history. In this review, tailored for biologists with a standard background in mathematics entering the field of population genomics, we explain how signatures of positive selection emerge and describe the principles of state-of-the-art programs to detect these signatures. We highlight the promises and pitfalls of all approaches and provide practical recommendations based on simulation studies as well as various case studies from animals.