Decoding Genomic Landscapes of Introgression

2025 · doi:10.32942/x2ph00
preprint OA: closed CC-BY-NC-ND-4.0
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Abstract

Genomic landscapes of introgression provide valuable information for how different evolutionary processes interact and leave signatures in genomes. The recent expansion of genomic datasets across diverse taxa, together with advances in methodological development, has created new opportunities to investigate the impact of introgression along individual genomes in various clades, making the precise identification of introgressed loci a rapidly evolving area of research. In this review, we summarize recent methodological progress within three major categories: summary statistics, probabilistic modeling, and supervised learning. We examine how these approaches have been applied to data beyond humans and discuss the challenges associated with their application. Finally, we outline future directions for each category, including accessible implementation, transparent analysis, and systematic benchmarking.
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Abstract

Genomic landscapes of introgression provide valuable information for how different evolutionary processes interact and leave signatures in genomes. The recent expansion of genomic datasets across diverse taxa, together with advances in methodological development, has created new opportunities to investigate the impact of introgression along individual genomes in various clades, making the precise identification of introgressed loci a rapidly evolving area of research. In this review, we summarize recent methodological progress within three major categories: summary statistics, probabilistic modeling, and supervised learning. We examine how these approaches have been applied to data beyond humans and discuss the challenges associated with their application. Finally, we outline future directions for each category, including accessible implementation, transparent analysis, and systematic benchmarking. DOI https://doi.org/10.32942/X2PH00 Subjects Life Sciences

Keywords

introgression, admixture, Population genetics, machine learning Dates Published: 2025-05-23 16:00 Last Updated: 2025-07-24 15:57 Older Versions License CC-By Attribution-NonCommercial-NoDerivatives 4.0 International Additional Metadata Language: English

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License: CC-BY-NC-ND-4.0