Predicting geographic location from genetic variation with deep neural networks

Abstract
Data availability
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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.

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).

The following previously published data sets were used

1. The Anopheles gambiae 1000 Genomes Consortium
(2015) Ag1000G phase 1 AR3 data release
MalariaGEN, http://www.malariagen.net/data/ag1000g-phase1-AR3.

https://www.malariagen.net/data/ag1000g-phase1-ar3
1. Plasmodium facliparum community project
(2019) Plasmodium falciparum community project version 6 data release
MalariaGEN, https://www.malariagen.net/resource/26.

https://www.malariagen.net/resource/26
(2019) Insights into human genetic variation and population history from 929 diverse genomes
HGDP, ftp://ngs.sanger.ac.uk/production/hgdp.

ftp://ngs.sanger.ac.uk/production/hgdp

Article and author information

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.

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.