Inferring historical introgression with deep learning

Resolving the phylogenetic relationships among taxa remains a challenge in the era of big data due to the presence of genetic admixture in a wide range of organisms. Rapidly developing sequencing technologies and statistical tests enable evolutionary relationships to be disentangled at a genome-wide level, yet many of these tests are computationally intensive and rely on phased genotypes, large sample sizes, restricted phylogenetic topologies, or hypothesis testing. To overcome these difficulties, we developed a deep learning-based approach, named ERICA, for inferring genome-wide evolutionary relationships and local introgressed regions from sequence data. ERICA accepts sequence alignments of both population genomic data and multiple genome assemblies, and efficiently identifies discordant genealogy patterns and exchanged regions across genomes when compared with other methods. We further tested ERICA using real population genomic data from Heliconius butterflies that have undergone ada..., ,