Predicting geographic location from genetic variation with deep neural networks
- University of Oregon, United States
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.
Data availability
Locator is implemented as a command-line program written in Python: www.github.com/kern-lab/locator. SNP calls for the Anopheles dataset are available at https://www.malariagen.net/data/ag1000g-phase1-ar3, for P. falciparum at https://www.malariagen.net/resource/26,and for the HGDP at ftp://ngs.sanger.ac.uk/production/hgdp. Code to run continuous-space simulations can be found at https://github.com/kern-lab/spaceness/blob/master/slim_recipes/spaceness.slim. This publication uses data from the MalariaGEN Plasmodium falciparum Community Project as described in Pearson et al. (2019). Statistical analyses and many plots were produced in R (R Core Team, 2018).
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Ag1000G phase 1 AR3 data releaseMalariaGEN, http://www.malariagen.net/data/ag1000g-phase1-AR3.
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Plasmodium falciparum community project version 6 data releaseMalariaGEN, https://www.malariagen.net/resource/26.
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Insights into human genetic variation and population history from 929 diverse genomesHGDP, ftp://ngs.sanger.ac.uk/production/hgdp.
Article and author information
Author details
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
Version 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
© 2020, Battey et al.
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.
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Predicting geographic location from genetic variation with deep neural networks
eLife 9:e54507.
https://doi.org/10.7554/eLife.54507
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