Identifying targets of selection in mosaic genomes with machine learning: applications in Anopheles gambiae for detecting sites within locally adapted chromosomal inversions

Chromosomal inversions are important structural changes that may facilitate divergent selection when they capture co-adaptive loci in the face of gene flow. However, identifying selection targets within inversions can be challenging. The high degrees of differentiation between heterokaryotypes, as well as the differences in demographic histories of collinear regions compared with inverted ones, reduce the power of traditional outlier analyses for detecting selected loci. Here, we develop a new approach that uses discriminant functions informed from inversion-specific expectations to classify loci that are under selection (or drift). Analysis of RAD sequencing data we collected in a classic dipteran species with polymorphic inversion clines-Anopheles gambiae, a malaria vector species from sub-Saharan Africa-demonstrates the benefits of the approach compared with traditional outlier analyses. We focus specifically on two polymorphic inversions, the 2La and 2Rb arrangements that predominat...