1. Computational and Systems Biology
  2. Genetics and Genomics
Download icon

Detecting adaptive introgression in human evolution using convolutional neural networks

  1. Graham Gower  Is a corresponding author
  2. Pablo Iáñez Picazo
  3. Matteo Fumagalli
  4. Fernando Racimo
  1. University of Copenhagen, Denmark
  2. Imperial College London, United Kingdom
Tools and Resources
  • Cited 1
  • Views 1,852
  • Annotations
Cite this article as: eLife 2021;10:e64669 doi: 10.7554/eLife.64669

Abstract

Studies in a variety of species have shown evidence for positively selected variants introduced into a population via introgression from another, distantly related population - a process known as adaptive introgression. However, there are few explicit frameworks for jointly modelling introgression and positive selection, in order to detect these variants using genomic sequence data. Here, we develop an approach based on convolutional neural networks (CNNs). CNNs do not require the specification of an analytical model of allele frequency dynamics, and have outperformed alternative methods for classification and parameter estimation tasks in various areas of population genetics. Thus, they are potentially well suited to the identification of adaptive introgression. Using simulations, we trained CNNs on genotype matrices derived from genomes sampled from the donor population, the recipient population and a related non-introgressed population, in order to distinguish regions of the genome evolving under adaptive introgression from those evolving neutrally or experiencing selective sweeps. Our CNN architecture exhibits 95% accuracy on simulated data, even when the genomes are unphased, and accuracy decreases only moderately in the presence of heterosis. As a proof of concept, we applied our trained CNNs to human genomic datasets - both phased and unphased - to detect candidates for adaptive introgression that shaped our evolutionary history.

Data availability

Source code is available from https://github.com/grahamgower/genomatnn/.

The following previously published data sets were used

Article and author information

Author details

  1. Graham Gower

    Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
    For correspondence
    graham.gower@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6197-3872
  2. Pablo Iáñez Picazo

    Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  3. Matteo Fumagalli

    Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4084-2953
  4. Fernando Racimo

    Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5025-2607

Funding

Villum Fonden (00025300)

  • Fernando Racimo

Leverhulme Trust (RPG-2018-208)

  • Matteo Fumagalli

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

Reviewing Editor

  1. George H Perry, Pennsylvania State University, United States

Publication history

  1. Received: November 6, 2020
  2. Accepted: May 24, 2021
  3. Accepted Manuscript published: May 25, 2021 (version 1)
  4. Version of Record published: June 10, 2021 (version 2)

Copyright

© 2021, Gower 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

  • 1,852
    Page views
  • 234
    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. Computational and Systems Biology
    2. Epidemiology and Global Health
    Hannah R Meredith et al.
    Research Article

    Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.

    1. Computational and Systems Biology
    Daniel Griffith, Alex S Holehouse
    Tools and Resources

    The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex nonlinear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.