1. Evolutionary Biology
  2. Genetics and Genomics
Download icon

Predicting geographic location from genetic variation with deep neural networks

  1. CJ Battey  Is a corresponding author
  2. Peter L Ralph
  3. Andrew D Kern
  1. University of Oregon, United States
Tools and Resources
  • Cited 11
  • Views 4,362
  • Annotations
Cite this article as: eLife 2020;9:e54507 doi: 10.7554/eLife.54507

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

Article and author information

Author details

  1. 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.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9958-4282
  2. Peter L Ralph

    Institute of Ecology and Evolution, University of Oregon, Eugene, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Andrew D Kern

    Institute of Ecology and Evolution, University of Oregon, Eugene, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "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

  1. Magnus Nordborg, Austrian Academy of Sciences, Austria

Publication history

  1. Received: December 17, 2019
  2. Accepted: June 3, 2020
  3. Accepted Manuscript published: June 8, 2020 (version 1)
  4. 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.

Metrics

  • 4,362
    Page views
  • 391
    Downloads
  • 11
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Evolutionary Biology
    2. Genetics and Genomics
    Jason Laurich, Anna M O'Brien
    Insight

    In the common sunflower, patterns of UV-absorbing pigments are controlled by a newly identified regulatory region and may be under the influence of environmental factors.

    1. Developmental Biology
    2. Evolutionary Biology
    Yushi Wu et al.
    Research Article

    Gene regulatory networks coordinate the formation of organs and structures that compose the evolving body plans of different organisms. We are using a simple chordate model, the Ciona embryo, to investigate the essential gene regulatory network that orchestrates morphogenesis of the notochord, a structure necessary for the proper development of all chordate embryos. Although numerous transcription factors expressed in the notochord have been identified in different chordates, several of them remain to be positioned within a regulatory framework. Here we focus on Xbp1, a transcription factor expressed during notochord formation in Ciona and other chordates. Through the identification of Xbp1-downstream notochord genes in Ciona, we found evidence of the early co-option of genes involved in the unfolded protein response to the notochord developmental program. We report the regulatory interplay between Xbp1 and Brachyury, and by extending these results to Xenopus, we show that Brachyury and Xbp1 form a cross-regulatory subcircuit of the notochord gene regulatory network that has been consolidated during chordate evolution.