ArticleeLife2021
Detecting adaptive introgression in human evolution using convolutional neural networks.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed, 102 citations in OpenAlex.
- Genomic insights into natural selection in recent human history.Nature reviews. Genetics · 2026Review
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- What has population genomics told us about the dynamics of selection and plant adaptation?Molecular biology and evolution · 2026Review
- Neural posterior estimation for population genetics.Genetics · 2026Article
- ERICA-trio: an outgroup-free deep learning method for topology inference and introgression detection.Briefings in bioinformatics · 2026Article
- Long-term isolation and archaic introgression shape functional genetic variation in Near Oceania.Science (New York, N.Y.) · 2026Article
- AI solutions for evolutionary genomics of nonmodel species.Evolution letters · 2026Review
- Detecting Positive Selection by Modeling Structure Within Images of Genetic Variation.Genome biology and evolution · 2026Article
- Identifying Adaptive Footprints in the Presence of Demographic Uncertainty.Genome biology and evolution · 2026Article
- Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026Article
- Summary statistics and approximate bayesian computation are comparable to convolutional neural networks for inferring times to fixation.bioRxiv : the preprint server for biology · 2026Article
- Recent advances in methods to characterize archaic introgression in modern humans.Genome research · 2026Review
- Do syngameons exist in tropical trees? Challenges to determine their existence and estimate their frequency.Biology letters · 2025Review
- Interpreting Supervised Machine Learning Inferences in Population Genomics Using Haplotype Matrix Permutations.Molecular biology and evolution · 2025Article
- Article
- Efficient Detection and Characterization of Targets of Natural Selection Using Transfer Learning.Molecular biology and evolution · 2025Article
- ConfuseNN: Interpreting convolutional neural network inferences in population genomics with data shuffling.bioRxiv : the preprint server for biology · 2025Article
- Efficient detection and characterization of targets of natural selection using transfer learning.bioRxiv : the preprint server for biology · 2025Article
- Archaic introgression and the distribution of shared variation under stabilizing selection.PLoS genetics · 2025Article
- Archaic hominin admixture and its consequences for modern humans.Current opinion in genetics & development · 2025Review
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Studies in a variety of species have shown evidence for positively selected variants introduced into a population via introgression from another, distantly related population-a process known as adaptive introgression. However, there are few explicit frameworks for jointly modelling introgression and positive selection, in order to detect these variants using genomic sequence data. Here, we develop an approach based on convolutional neural networks (CNNs). CNNs do not require the specification of an analytical model of allele frequency dynamics and have outperformed alternative methods for classification and parameter estimation tasks in various areas of population genetics. Thus, they are potentially well suited to the identification of adaptive introgression. Using simulations, we trained CNNs on genotype matrices derived from genomes sampled from the donor population, the recipient population and a related non-introgressed population, in order to distinguish regions of the genome evolving under adaptive introgression from those evolving neutrally or experiencing selective sweeps. Our CNN architecture exhibits 95% accuracy on simulated data, even when the genomes are unphased, and accuracy decreases only moderately in the presence of heterosis. As a proof of concept, we applied our trained CNNs to human genomic datasets-both phased and unphased-to detect candidates for adaptive introgression that shaped our evolutionary history.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.