Decoding genomic landscapes of introgression

Recent advances in methods and tools have enabled the study of genomic landscapes of introgression across diverse and complex evolutionary scenarios, including adaptive and ghost introgression. 

Despite their long history, summary statistics-based methods continue to evolve, with new implementations broadening their applicability across taxa.

Probabilistic modeling is a major approach that provides a powerful framework to explicitly incorporate evolutionary processes and has yielded fine-scale insights across diverse species.

Supervised learning is an emerging approach with great potential, particularly when the detection of introgressed loci is framed as a semantic segmentation task.

Various methods have been applied across clades, revealing introgressed loci linked to immunity, reproduction, and environmental adaptation, especially in cases of adaptive and ghost introgression.

https://www.cell.com/trends/genetics/fulltext/S0168-9525(25)00166-0

https://sciencemission.com/genomic-landscapes-of-introgression