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 adaptive
radiation and frequent hybridization. Finally, we applied ERICA to
characterize hybridization and introgression in wild and cultivated rice,
revealing the important role of introgression in rice domestication and
adaptation. Taken together, our findings demonstrate that ERICA provides
an effective method for teasing apart evolutionary relationships using
whole genome data, which can ultimately facilitate evolutionary
studies on hybridization and introgression.