️ Episode 161: Decoding Genomic Landscapes of Introgression

In this episode of PaperCast Base by Base, we explore how modern population genetics dissects the genomic footprints of introgression across species, reviewing summary statistic approaches, probabilistic modeling, and supervised learning, and showing how these methods reveal adaptive and ghost introgression and the functional roles of introgressed loci.

Study Highlights:
The authors organize the field into three complementary pillars: summary statistics for fast exploratory scans, probabilistic models for principled inference of local ancestry and selection, and supervised deep learning for scalable, high‑resolution predictions. They explain why windowed statistics such as fd, df, and fdM improve on D for localizing introgressed loci and how methods like S*, S′, and topology weighting tackle ghost introgression and gene‑tree discordance. They show that probabilistic tools including IBDmix, VolcanoFinder, HMM‑based local ancestry, and ARG‑based frameworks can quantify fragment properties and selection while handling complex scenarios such as multi‑source and low‑coverage data. They highlight emerging CNN‑ and segmentation‑based models (e.g., IntroUNET) that operate on genotype matrices to mark introgressed alleles with fine resolution, alongside real‑world applications beyond humans that implicate loci tied to immunity, reproduction, and environmental adaptation.

Conclusion:
Together, these approaches map introgression at increasing resolution and generality, and the field is moving toward transparent, benchmarked, and accessible tools that integrate statistics, probabilistic modeling, and machine learning to decode how gene flow shapes genomes across the tree of life.

Reference:
Huang X, Hackl J, Kuhlwilm M (2025) Decoding genomic landscapes of introgression. Trends in Genetics. https://doi.org/10.1016/j.tig.2025.07.001

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

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