Performance evaluation of adaptive introgression classification methods

Abstract

AbstractIntrogression, the incorporation of foreign variants through hybridization and repeated backcross, is increasingly being studied for its potential evolutionary consequences, one of which is adaptive introgression (AI). In recent years, several statistical methods have been proposed for the detection of loci that have undergone adaptive introgression. Most of these methods have been tested and developed to infer the presence of Neanderthal or Denisovan AI in humans. Currently, the behaviour of these methods when faced with genomic datasets from evolutionary scenarios other than the human lineage remains unknown. This study therefore focuses on testing the performance of the methods using test data sets simulated under an evolutionary scenario inspired by a complex of hybridising lizard species in the Iberian Peninsula:Podarcis hispanicus. Our tests focus on analysing the impact of variations in the strength of selection on the performance of three methods (VolcanoFinder,GenomatnnandMaLAdapt) and a standalone summary statistic (Q95). Furthermore, the hitchhiking effect of the adaptively introgressed mutation can have a strong impact on the flanking regions, and therefore on the best differentiation between the genomic windows classes (i.e. AI/non-AI). For this reason, three different types of non-AI windows are therefore taken into account in our analyses: independently simulated neutral introgression windows, windows adjacent to the window under AI and windows coming from a second neutral chromosome unlinked to the chromosome under AI. Our results highlight the importance of taking into account adjacent windows in the training data in order to correctly identify the window with the mutation under AI. Finally, our tests show that methods based on Q95 seem to be the most efficient for an exploratory study of AI (i.e. without any a priori on the presence of AI).