Bayesian test of gene flow between sister lineages using genomic data.

Area of Science:

• Genomics
• Evolutionary Biology
• Bioinformatics

Background:

• Interspecific gene flow is vital for reconstructing species phylogenies and understanding speciation.
• Detecting gene flow between sister lineages is difficult for heuristic methods.
• Likelihood-based methods can detect introgression between sisters but present statistical challenges.

Purpose of the Study:

• To develop a robust Bayesian method for inferring gene flow between sister lineages.
• To address nonstandard statistical features in introgression tests.
• To provide a reliable method for assessing evidence of gene flow between closely related species.

Main Methods:

• Developed a theory for applying the Savage-Dickey (S-D) density ratio under nonstandard conditions.
• Utilized a Bayesian framework to test for introgression between sister lineages.
• Applied the method to a genomic dataset from Sceloporus lizards.

Main Results:

• The Bayesian test demonstrates low false-positive rates and high statistical power for detecting gene flow between sister lineages.
• Species split time significantly impacts data information content, with deeper divergence providing more information.
• The study provides a framework for estimating gene flow rates, especially at extreme values.

Conclusions:

• The developed Bayesian test offers a reliable approach for assessing gene flow between sister lineages.
• Genomic data, particularly from deeper divergences, is informative for inferring interspecific gene flow.
• This method enhances our understanding of speciation processes by accurately detecting introgression between closely related species.