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. Progress has been impeded by a conceptual and methodological divide between analyses that infer the demographic history of speciation and genome scans aimed at identifying locally maladaptive alleles i.e. genomic barriers to gene flow. Here we implement genomewide IM blockwise likelihood estimation (gIMble), a composite likelihood approach for the quantification of barriers, that bridges this divide. This analytic framework captures background selection and selection against 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. gIMble 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 pair of sister species of tropical butterflies with a known history of post-divergence gene flow: Heliconius melpomene and H. cydno. Our analyses uncover both large-effect barrier loci (including well-known wing-pattern genes) and a genome-wide signal of a polygenic barrier architecture. As a fundamental process generating biological diversity, speciation involves the evolution of reproductive isolation and thus the build-up of barriers to genetic exchange among organismal groups. While population genomic data are arguably the only source of information we have about most recent speciation events, the way such data are analysed remains depressingly superficial: population genomic studies of speciation are phrased either as scans for outliers of genetic differentiation, or are based on models of neutral evolution under the constraint of a single genome-wide demography. Here we introduce a new statistical framework called gIMble to estimate the effective rate of gene flow and the effective population sizes along the genome from population genomic data. By capturing genome-wide variation in these two effective demographic parameters, gIMble disentangles the genomic footprints of different modes of selection and provides a direct quantification of the species barrier. To illustrate this framework, we analyse a classic speciation genomic dataset from Heliconius butterflies. We show that barriers to gene flow in this system include both large effect loci-most, but not all, of which were known from functional work-as well as a genome-wide signature of weak-effect polygenic barriers.