ArticlePLoS genetics2018
Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia.
Article in PLoS genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.
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77 citing papers in PubMed, 1 synthesis or guideline pooled it, 125 citations in OpenAlex.
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- ERICA-trio: an outgroup-free deep learning method for topology inference and introgression detection.Briefings in bioinformatics · 2026Article
- Deep learning reveals genomic regions introgressed between two recurrently hybridizing lynx species.Molecular biology and evolution · 2026Article
- Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026Article
- Recent advances in methods to characterize archaic introgression in modern humans.Genome research · 2026Review
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- Towards a Research Programme Aiming at Causes and Consequences of Reticulate Evolution.Biology · 2025Review
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- Comparative single-cell transcriptomic atlases of drosophilid brains suggest glial evolution during ecological adaptation.PLoS biology · 2025Article
- Genomic Islands of Divergence Between Drosophila yakuba Subspecies are Predominantly Driven by Chromosomal Inversions and the Recombination Landscape.Molecular ecology · 2025Article
- Double trouble: two retrotransposons triggered a cascade of invasions in Drosophila species within the last 50 years.Nature communications · 2025Article
- Tree Sequences as a General-Purpose Tool for Population Genetic Inference.Molecular biology and evolution · 2024Article
- Article
17 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Hybridization and gene flow between species appears to be common. Even though it is clear that hybridization is widespread across all surveyed taxonomic groups, the magnitude and consequences of introgression are still largely unknown. Thus it is crucial to develop the statistical machinery required to uncover which genomic regions have recently acquired haplotypes via introgression from a sister population. We developed a novel machine learning framework, called FILET (Finding Introgressed Loci via Extra-Trees) capable of revealing genomic introgression with far greater power than competing methods. FILET works by combining information from a number of population genetic summary statistics, including several new statistics that we introduce, that capture patterns of variation across two populations. We show that FILET is able to identify loci that have experienced gene flow between related species with high accuracy, and in most situations can correctly infer which population was the donor and which was the recipient. Here we describe a data set of outbred diploid Drosophila sechellia genomes, and combine them with data from D. simulans to examine recent introgression between these species using FILET. Although we find that these populations may have split more recently than previously appreciated, FILET confirms that there has indeed been appreciable recent introgression (some of which might have been adaptive) between these species, and reveals that this gene flow is primarily in the direction of D. simulans to D. sechellia.
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