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 1
  • Views 3,080
  • 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

  • 3,080
    Page views
  • 306
    Downloads
  • 1
    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
    Claudia Igler et al.
    Research Article Updated

    The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: (i) a single mutation, which provides a large resistance benefit, or (ii) multiple mutations, each conferring a small benefit, which combine to yield high-level resistance. Using stochastic modeling, we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the extent of this limitation depends on the combination of drug type and pharmacokinetic profile. Further, if multiple mutations are necessary, adaptive treatment, which only suppresses the bacterial population, delays treatment failure due to resistance for a longer time than aggressive treatment, which aims at eradication.

    1. Evolutionary Biology
    2. Physics of Living Systems
    Joy Bergelson et al.
    Review Article

    The immeasurable complexity at every level of biological organization creates a daunting task for understanding biological function. Here, we highlight the risks of stripping it away at the outset and discuss a possible path toward arriving at emergent simplicity of understanding while still embracing the ever-changing complexity of biotic interactions that we see in nature.