RIDGE, a tool tailored to detect gene flow barriers across species pairs.

Characterizing the processes underlying reproductive isolation between diverging lineages is central to understanding speciation. Here, we present RIDGE-Reproductive Isolation Detection using Genomic polymorphisms-a tool tailored for quantifying gene flow barrier proportion and identifying the relevant genomic regions. RIDGE relies on an Approximate Bayesian Computation with a model-averaging approach to accommodate diverse scenarios of lineage divergence. It captures heterogeneity in effective migration rate along the genome while accounting for variation in linked selection and recombination. The barrier detection test relies on numerous summary statistics to compute a Bayes factor, offering a robust statistical framework that facilitates cross-species comparisons. Simulations revealed RIDGE's efficiency in capturing signals of ongoing migration. Model averaging proved particularly valuable in scenarios of high model uncertainty where no migration or migration homogeneity can be wrongly assumed, typically for recent divergence times <0.1 2Ne generations. Applying RIDGE to four published crow data sets, we first validated our tool by identifying a well-known large genomic region associated with mate choice patterns. Second, while we identified a significant overlap of outlier loci using RIDGE and traditional genomic scans, our results suggest that a substantial portion of previously identified outliers are likely false positives. Outlier detection relies on allele differentiation, relative measures of divergence and the count of shared polymorphisms and fixed differences. Our analyses also highlight the value of incorporating multiple summary statistics including our newly developed outlier ones that can be useful in challenging detection conditions.

Gene network simulations provide testable predictions for the molecular domestication syndrome.

The domestication of plant species leads to repeatable morphological evolution, often referred to as the phenotypic domestication syndrome. Domestication is also associated with important genomic changes, such as the loss of genetic diversity compared with adequately large wild populations, and modifications of gene expression patterns. Here, we explored theoretically the effect of a domestication-like scenario on the evolution of gene regulatory networks. We ran population genetics simulations in which individuals were featured by their genotype (an interaction matrix encoding a gene regulatory network) and their gene expressions, representing the phenotypic level. Our domestication scenario included a population bottleneck and a selection switch mimicking human-mediated directional and canalizing selection, i.e., change in the optimal gene expression level and selection toward more stable expression across environments. We showed that domestication profoundly alters genetic architectures. Based on four examples of plant domestication scenarios, our simulations predict (1) a drop in neutral allelic diversity; (2) a change in gene expression variance that depends upon the domestication scenario; (3) transient maladaptive plasticity; (4) a deep rewiring of the gene regulatory networks, with a trend toward gain of regulatory interactions; and (5) a global increase in the genetic correlations among gene expressions, with a loss of modularity in the resulting coexpression patterns and in the underlying networks. We provide empirically testable predictions on the differences of genetic architectures between wild and domesticated forms. The characterization of such systematic evolutionary changes in the genetic architecture of traits contributes to define a molecular domestication syndrome.