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dc.contributor.authorLiu, Kevin J.
Dai, Jingxuan
Truong, Kathy
Song, Ying
Kohn, Michael H.
Nakhleh, Luay
dc.date.accessioned 2014-10-08T21:25:38Z
dc.date.available 2014-10-08T21:25:38Z
dc.date.issued 2014
dc.identifier.citation Liu, Kevin J., Dai, Jingxuan, Truong, Kathy, et al.. "An HMM-Based Comparative Genomic Framework for Detecting Introgression in Eukaryotes." PLoS Computational Biology, 10, no. 6 (2014) Public Library of Science: e1003649. http://dx.doi.org/10.1371/journal.pcbi.1003649.
dc.identifier.urihttps://hdl.handle.net/1911/77461
dc.description.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.
dc.language.iso eng
dc.publisher Public Library of Science
dc.rights This 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.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title An HMM-Based Comparative Genomic Framework for Detecting Introgression in Eukaryotes
dc.type Journal article
dc.contributor.funder National Institutes of Health
dc.contributor.funder U.S. National Library of Medicine
dc.contributor.funder National Science Foundation
dc.contributor.funder Alfred P. Sloan Foundation
dc.contributor.funder John Simon Guggenheim Memorial Foundation
dc.citation.journalTitle PLoS Computational Biology
dc.citation.volumeNumber 10
dc.citation.issueNumber 6
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1371/journal.pcbi.1003649
dc.identifier.pmcid PMC4055573
dc.identifier.pmid 24922281
dc.identifier.grantID R01-HL091007-01A1 (National Institutes of Health)
dc.identifier.grantID W.M. Keck Center for Interdisciplinary Bioscience Training
dc.identifier.grantID T15LM007093 (U.S. National Library of Medicine)
dc.identifier.grantID DBI-1062463 (National Science Foundation)
dc.identifier.grantID CCF-1302179 (National Science Foundation)
dc.identifier.grantID R01LM009494 (U.S. National Library of Medicine)
dc.identifier.grantID Research Fellowship (Alfred P. Sloan Foundation)
dc.identifier.grantID Guggenheim Fellowship (John Simon Guggenheim Memorial Foundation)
dc.type.publication publisher version
dc.citation.firstpage e1003649


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