Detecting adaptive introgression in human evolution using convolutional neural networks

Abstract
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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

1. The 1000 Genomes Project Consortium
(2015) A global reference for human genetic variation
10.1038/nature15393.

http://www.nature.com/nature/journal/v526/n7571/full/nature15393.html
1. Jacobs et al.
(2019) Multiple deeply divergent Denisovan ancestries in Papuans
10.1016/j.cell.2019.02.035.

http://www.sciencedirect.com/science/article/pii/S0092867419302181

Article and author information

Author details

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.

"This ORCID iD identifies the author of this article:" 0000-0002-6197-3872
Pablo Iáñez Picazo

Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark

Competing interests
The authors declare that no competing interests exist.
Matteo Fumagalli

Imperial College London, London, United Kingdom

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-4084-2953
Fernando Racimo

Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark

Competing interests
The authors declare that no competing interests exist.

"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.

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.