Introgression refers to the phenomenon of transferring genetic material from one taxon (often a species or population) into the genetic background of another by hybridisation and then successive backcrossing. If the introgression brings a selective advantage to the recipient taxon, it is called adaptive introgression (AI). In this context, my thesis aims to develop and test different methodological approaches to identify regions under AI. First, I will develop a simulation-based inference method exploring the variations in the level of introgression between neutral and adaptive nuclear regions. In a second step, I will design an inferential method to directly identify regions under AI. The analysis of genomic data from the complex of lizard species (Podarcis) hybridizing in the Iberian Peninsula will allow me to test the different methods developed on a real data set.