Detecting cryptic ghost lineage introgression in four-taxon genomic datasets.

Area of Science:

• Evolutionary Biology
• Genomics
• Bioinformatics

Background:

• Hybridization and introgression are key drivers of plant diversity.
• Existing methods for detecting introgression can be misled by ghost lineage introgression, obscuring the identities of donor and recipient lineages.
• This ambiguity hinders understanding of introgression mechanisms and functional impacts.

Purpose of the Study:

• To develop a statistical test, named ghostbuster, for detecting ghost lineage introgression using genomic data.
• To assess the accuracy of ghostbuster in distinguishing between ingroup and ghost lineage introgression under various evolutionary scenarios.
• To apply ghostbuster to empirical data, specifically an introgression event in Brassicaceae, to demonstrate its utility.

Main Methods:

• Development of the ghostbuster statistical test based on sequence divergence patterns.
• Utilizing coalescent simulations to evaluate ghostbuster's performance and identify conditions for accurate introgression detection.
• Application of ghostbuster to a known introgression event within the Brassicaceae plant family.

Main Results:

• Ghostbuster accurately differentiates ghost lineage introgression from ingroup introgression across diverse scenarios.
• Method errors occur primarily with closely spaced divergence events or unbalanced ancestral population sizes.
• Analysis of Brassicaceae data revealed a previously misinterpreted introgression event as ghost lineage introgression.

Conclusions:

• Ghostbuster is a valuable tool for resolving reticulate evolution in plants and other organisms.
• The study provides updated insights into ghost lineage introgression in Brassicaceae, impacting the understanding of crop and model species evolution.
• The ghostbuster software is publicly available for further research.