{"title":"Decoding genomic landscapes of introgression.","authors":"Xin Huang, Josef Hackl, Martin Kuhlwilm","doi":"10.1016/j.tig.2025.07.001","DOIUrl":null,"url":null,"abstract":"<p><p>Genomic landscapes of introgression provide valuable information on 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, have 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.</p>","PeriodicalId":54413,"journal":{"name":"Trends in Genetics","volume":" ","pages":""},"PeriodicalIF":16.3000,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.tig.2025.07.001","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Genomic landscapes of introgression provide valuable information on 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, have 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.
期刊介绍：
Launched in 1985, Trends in Genetics swiftly established itself as a "must-read" for geneticists, offering concise, accessible articles covering a spectrum of topics from developmental biology to evolution. This reputation endures, making TiG a cherished resource in the genetic research community. While evolving with the field, the journal now embraces new areas like genomics, epigenetics, and computational genetics, alongside its continued coverage of traditional subjects such as transcriptional regulation, population genetics, and chromosome biology.
Despite expanding its scope, the core objective of TiG remains steadfast: to furnish researchers and students with high-quality, innovative reviews, commentaries, and discussions, fostering an appreciation for advances in genetic research. Each issue of TiG presents lively and up-to-date Reviews and Opinions, alongside shorter articles like Science & Society and Spotlight pieces. Invited from leading researchers, Reviews objectively chronicle recent developments, Opinions provide a forum for debate and hypothesis, and shorter articles explore the intersection of genetics with science and policy, as well as emerging ideas in the field. All articles undergo rigorous peer-review.
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