ArticleeLife2020
Predicting geographic location from genetic variation with deep neural networks.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
70 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Estimating human mobility in Holocene Western Eurasia with large-scale ancient genomic data.Proceedings of the National Academy of Sciences of the United States of America · 2023Pooled it
- Inferring Geographic Origin of White-Tailed Deer (Ecology and evolution · 2026Article
- Harnessing Genomic Information for Identifying the Geographic Origin of Five North American Tree Species in Trade.Evolutionary applications · 2026Article
- Targeted chromosomal sequencing of wild bonobos identifies a genetically distinct subpopulation east of the Lomami river.Genome biology · 2026Article
- On the use of generative models for demographic inference in malaria vectors from genomic data.G3 (Bethesda, Md.) · 2026Article
- Large-scale human genomic resources drive innovations toward forensic intelligent source attribution and precision identification.International journal of legal medicine · 2026Review
- Unlocking the Past to Protect the Future: Forensic DNA and Historical Specimens Reveal the Origins of Smuggled Yellow-Headed Parrots.Evolutionary applications · 2026Article
- Targeted sequencing enhances detection of pangolin trafficking hotspots and dynamics of both domestic and global trade markets.PLoS biology · 2026Article
- Coalescence and translation: A language model for population genetics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Genomic Signatures of Microgeographic Adaptation in Anopheles coluzzii Across Urban, Rural, and Forested Environments in Gabon.Molecular ecology · 2026Article
- Identification of Genetic Variation Associated With Heat Tolerance in Cowpea (Vigna unguiculata L. Walp.).Molecular ecology · 2026Article
- Reflections on the Human Genome Diversity Project: a conversation with Marcus W. Feldman, Henry T. Greely, and Mary-Claire King.Genetics · 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
- Autoencoders for genomic variation analysis.Genome research · 2026Article
- Integrating mosquito genomics into simulation modeling: opportunities for better-informed biocontrol.Current opinion in insect science · 2026Review
- Targeted Chromosomal Sequencing of Wild Bonobos Identifies a Genetically Distinct Subpopulation East of the Lomami River.bioRxiv : the preprint server for biology · 2026Article
- Fifteenth century CE Bolivian maize reveals genetic affinities with ancient Peruvian maize.eLife · 2025Article
- Integrated genetic and geographic ancestry prediction via large-scale genomic data and machine learning.Human genomics · 2025Article
- Regulating genome language models: navigating policy challenges at the intersection of AI and genetics.Human genetics · 2025Review
- Jointly representing long-range genetic similarity and spatially heterogeneous isolation-by-distance.PLoS genetics · 2025Article
10 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
3 authors.
Funding
Abstract
Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allows us to predict the location of origin of a genetic sample by comparing it to a set of samples of known geographic origin. Here, we describe a deep learning method, which we call Locator, to accomplish this task faster and more accurately than existing approaches. In simulations, Locator infers sample location to within 4.1 generations of dispersal and runs at least an order of magnitude faster than a recent model-based approach. We leverage Locator's computational efficiency to predict locations separately in windows across the genome, which allows us to both quantify uncertainty and describe the mosaic ancestry and patterns of geographic mixing that characterize many populations. Applied to whole-genome sequence data from
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Registered trials
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.