Detecting adaptive introgression in human evolution using convolutional neural networks.

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

SUBMITTER: Gower G 

PROVIDER: S-EPMC8192126 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature