161: Decoding genomic landscapes of introgression

Huang X et al., Trends in Genetics - A concise review of methodological advances for identifying introgressed loci across genomes. The article surveys summary statistics, probabilistic modeling, and supervised learning approaches, their applications beyond humans, and the main challenges for robust inference and software implementation. Key terms: introgression, genomic-landscapes, probabilistic-modeling, supervised-learning, summary-statistics.

Study Highlights:
The review organizes recent progress into three methodological categories: summary statistics, probabilistic models, and supervised learning, highlighting complementary strengths. It documents applications across diverse taxa, including examples of adaptive and ghost introgression. The authors note key challenges such as confounding by ILS and population structure, model misspecification, and analysis opacity. They call for accessible implementations, transparent workflows, and systematic benchmarking to improve reliability.

Conclusion:
Progress requires leveraging complementary methods, improving transparency and benchmarking, and developing robust, accessible tools that scale across taxa and complex evolutionary scenarios.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
Genomic landscapes of introgression

First author:
Huang X

Journal:
Trends in Genetics

DOI:
10.1016/j.tig.2025.07.001

Reference:
Huang X., Hackl J., Kuhlwilm M. Genomic landscapes of introgression. Trends in Genetics (2025). 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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