Identifying regions of the genome that act as barriers to gene flow between recently diverged taxa has remained challenging given the many evolutionary forces that generate variation in genetic diversity and divergence along the genome, and the stochastic nature of this variation. Here we implement a composite likelihood approach for the quantification of barriers to gene flow. This analytic framework captures background selection and selection against locally maladaptive alleles (i.e. genomic barriers) in a model of isolation with migration (IM) as heterogeneity in effective population size (Ne) and effective migration rate (me), respectively. Variation in both effective demographic parameters is estimated in sliding windows via pre-computed likelihood grids. We have implemented genomewide IM blockwise likelihood estimation (gIMble) as a modular tool, which includes modules for pre-processing/filtering of genomic data and performing parametric bootstraps using coalescent simulations. To demonstrate the new approach, we analyse data from a well-studied sister species pair of tropical butterflies with a known history of post-divergence gene flow: Heliconius melpomene and H. cydno. Our analysis uncovers both large effect barrier loci (including well-known wing-pattern genes) and a genome-wide signal of polygenic barrier architecture.

See manuscript: section Materials and Methods, subsection Heliconius Analyses.