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

Area of Science:

• Evolutionary Biology
• Genomics
• Speciation Research

Background:

• Understanding reproductive isolation is key to explaining how new species form.
• Genomic data offers powerful insights into the processes driving speciation.
• Existing methods for detecting genomic barriers to gene flow have limitations.

Purpose of the Study:

• To introduce RIDGE (Reproductive Isolation Detection using Genomic polymorphisms), a novel computational tool.
• To quantify the proportion of gene flow barriers and pinpoint associated genomic regions.
• To provide a robust framework for cross-species comparisons in speciation studies.

Main Methods:

• Utilizes Approximate Bayesian Computation with model averaging to handle diverse divergence scenarios.
• Accounts for genomic heterogeneity in migration rates, linked selection, and recombination.
• Employs a Bayes factor test based on multiple summary statistics, including novel outlier statistics.

Main Results:

• Simulations demonstrate RIDGE's effectiveness in detecting ongoing migration signals.
• Model averaging is crucial for recent divergence times (<0.1 2Ne generations) with high uncertainty.
• Application to crow data validated RIDGE by identifying known mate choice regions and revealed potential false positives in traditional outlier scans.

Conclusions:

• RIDGE provides a robust and versatile method for analyzing reproductive isolation and speciation.
• The tool enhances the accuracy of identifying genomic regions under selection due to reproductive isolation.
• Incorporating multiple summary statistics, especially novel ones, improves the reliability of genomic scans in speciation research.