Predicting geographic location from genetic variation with deep neural networks

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Cite this article as: eLife 2020;9:e54507 doi: 10.7554/eLife.54507

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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 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 Plasmodium parasites, Anopheles mosquitoes, and global human populations, this approach yields median test errors of 16.9km, 5.7km, and 85km, respectively.

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Author details

CJ Battey

Institute of Ecology and Evolution, University of Oregon, Eugene, United States

For correspondence
cjbattey@gmail.com

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-9958-4282
Peter L Ralph

Institute of Ecology and Evolution, University of Oregon, Eugene, United States

Competing interests
The authors declare that no competing interests exist.
Andrew D Kern

Institute of Ecology and Evolution, University of Oregon, Eugene, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-4381-4680

Funding

National Institutes of Health (R01GM117241)

CJ Battey
Andrew D Kern

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

Magnus Nordborg, Austrian Academy of Sciences, Austria

Publication history

Received: December 17, 2019
Accepted: June 3, 2020
Accepted Manuscript published: June 8, 2020 (version 1)
Version of Record published: June 29, 2020 (version 2)

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.