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

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dc.citation.firstpagee1003649en_US
dc.citation.issueNumber6en_US
dc.citation.journalTitlePLoS Computational Biologyen_US
dc.citation.volumeNumber10en_US
dc.contributor.authorLiu, Kevin J.en_US
dc.contributor.authorDai, Jingxuanen_US
dc.contributor.authorTruong, Kathyen_US
dc.contributor.authorSong, Yingen_US
dc.contributor.authorKohn, Michael H.en_US
dc.contributor.authorNakhleh, Luayen_US
dc.date.accessioned2014-10-08T21:25:38Zen_US
dc.date.available2014-10-08T21:25:38Zen_US
dc.date.issued2014en_US
dc.description.abstractOne 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.en_US
dc.identifier.citationLiu, Kevin J., Dai, Jingxuan, Truong, Kathy, et al.. "An HMM-Based Comparative Genomic Framework for Detecting Introgression in Eukaryotes." <i>PLoS Computational Biology,</i> 10, no. 6 (2014) Public Library of Science: e1003649. http://dx.doi.org/10.1371/journal.pcbi.1003649.en_US
dc.identifier.doihttp://dx.doi.org/10.1371/journal.pcbi.1003649en_US
dc.identifier.urihttps://hdl.handle.net/1911/77461en_US
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleAn HMM-Based Comparative Genomic Framework for Detecting Introgression in Eukaryotesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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