Data from: Demographically explicit scans for barriers to gene flow using gIMble

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.