An HMM-based comparative genomic framework for detecting introgression in eukaryotes

• Published in: PLoS computational biology 2014-06, Vol.10 (6), p.e1003649
• Main Authors: Liu, Kevin J, Dai, Jingxuan, Truong, Kathy, Song, Ying, Kohn, Michael H, Nakhleh, Luay
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Biology and Life Sciences; Cell research; Chromosome Mapping; Chromosomes; Comparative Genomic Hybridization; Computational Biology; Computer Simulation; Databases, Genetic; Eukaryotes; Evolution; Evolution, Molecular; Genetic aspects; Genetic engineering; Genetic research; Genetics, Population; Genomes; Genomics; Hidden Markov models; Hybridization; Markov Chains; Mice; Models, Genetic; Mutation; Phylogenetics; Phylogeny; Population; Scholarships & fellowships
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1003649

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.