An HMM-Based Comparative Genomic Framework for Detecting Introgression in Eukaryotes

Abstract

One outcome of interspecific hybridization and subsequent effects of evolutionary forces is introgression, which is the integration of genetic material from one species into the genome of an individual in another species. The evolution of several groups of eukaryotic species has involved hybridization, and cases of adaptation through introgression have been already established. In this work, we report on PhyloNet-HMM—a new comparative genomic framework for detecting introgression in genomes. PhyloNet-HMM combines phylogenetic networks with hidden Markov models (HMMs) to simultaneously capture the (potentially reticulate) evolutionary history of the genomes and dependencies within genomes. A novel aspect of our work is that it also accounts for incomplete lineage sorting and dependence across loci. Application of our model to variation data from chromosome 7 in the mouse (Mus musculus domesticus) genome detected a recently reported adaptive introgression event involving the rodent poison resistance gene Vkorc1, in addition to other newly detected introgressed genomic regions. Based on our analysis, it is estimated that about 9% of all sites within chromosome 7 are of introgressive origin (these cover about 13 Mbp of chromosome 7, and over 300 genes). Further, our model detected no introgression in a negative control data set. We also found that our model accurately detected introgression and other evolutionary processes from synthetic data sets simulated under the coalescent model with recombination, isolation, and migration. Our work provides a powerful framework for systematic analysis of introgression while simultaneously accounting for dependence across sites, point mutations, recombination, and ancestral polymorphism. Hybridization is the mating between individuals from two different species. While hybridization introduces genetic material into a host genome, this genetic material may be transient and is purged from the population within a few generations after hybridization. However, in other cases, the introduced genetic material persists in the population—a process known as introgression—and can have significant evolutionary implications. In this paper, we introduce a novel method for detecting introgression in genomes using a comparative genomic approach. The method scans multiple aligned genomes for signatures of introgression by incorporating phylogenetic networks and hidden Markov models. The method allows for teasing apart true signatures of introgression from spurious ones that arise due to population effects and resemble those of introgression. Using the new method, we analyzed two sets of variation data from chromosome 7 in mouse genomes. The method detected previously reported introgressed regions as well as new ones in one of the data sets. In the other data set, which was selected as a negative control, the method detected no introgression. Furthermore, our method accurately detected introgression in simulated evolutionary scenarios and accurately inferred related population genetic quantities. Our method enables systematic comparative analyses of genomes where introgression is suspected, and can work with genome-wide data.