RIDGE, a tool tailored to detect gene flow barriers across species pairs
2023
Burban, Ewen | Tenaillon, Maud, I | Glémin, Sylvain | Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO) ; Université de Rennes (UR)-Institut Ecologie et Environnement - CNRS Ecologie et Environnement (INEE-CNRS) ; Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS) | Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon) ; AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Abstract 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 proportions and identifying the corresponding 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 that RIDGE is particularly efficient both at capturing signals of ongoing migration and at identifying barrier loci, including for recent divergence times (~0.1 2 N e generations). Applying RIDGE to four published crow datasets, we validated our tool by identifying a well-known large genomic region associated with mate choice patterns. We identified additional barrier loci between species pairs, which have shown, on the one hand, that depending on the biological, demographic, and selection contexts, different combinations of summary statistics are informative for the detection of signals. On the other hand, these analyses also highlight the value of our newly developed outlier statistics in challenging detection conditions.
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