Detection of ghost introgression requires exploiting topological and branch length information

In recent years, the study of hybridization and introgression has made
significant progress, with ghost introgression—the transfer of genetic
material from extinct or unsampled lineages to extant species—emerging as
a key area for research. Accurately identifying ghost
introgression, however, presents a challenge. To address this issue, we
focused on simple cases involving three species with a known phylogenetic
tree. Using mathematical analyses and simulations, we evaluated the
performance of popular phylogenetic methods, including HyDe and
PhyloNet/MPL, and the full-likelihood method, Bayesian
Phylogenetics and Phylogeography (BPP), in detecting ghost introgression.
Our findings suggest that heuristic approaches relying on site-pattern
counts or gene-tree topologies struggle to differentiate ghost
introgression from introgression between sampled non-sister species,
frequently leading to incorrect identification of donor and recipient
species. The full-likelihood method BPP using multilocus sequence
alignments directly—hence taking into account both gene-tree topologies
and branch lengths, by contrast, is capable of detecting ghost
introgression in phylogenomic datasets. We analyzed a real-world
phylogenomic dataset of 14 species of Jaltomata (Solanaceae) to showcase
the potential of full-likelihood methods for accurate inference of
introgression.