RT Journal Article
SR Electronic
T1 Detecting adaptive introgression in human evolution using convolutional neural networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.09.18.301069
DO 10.1101/2020.09.18.301069
A1 Graham Gower
A1 Pablo Iáñez Picazo
A1 Matteo Fumagalli
A1 Fernando Racimo
YR 2020
UL http://biorxiv.org/content/early/2020/09/18/2020.09.18.301069.abstract
AB Studies in a variety of species have shown evidence for positively selected variants introduced into one 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 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.Competing Interest StatementThe authors have declared no competing interest.