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一种基于隐马尔可夫模型的用于检测真核生物基因渗入的比较基因组框架。

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

作者信息

Liu Kevin J, Dai Jingxuan, Truong Kathy, Song Ying, Kohn Michael H, Nakhleh Luay

机构信息

Department of Computer Science, Rice University, Houston, Texas, United States of America; Department of Ecology and Evolutionary Biology, Rice University, Houston, Texas, United States of America.

Department of Computer Science, Rice University, Houston, Texas, United States of America.

出版信息

PLoS Comput Biol. 2014 Jun 12;10(6):e1003649. doi: 10.1371/journal.pcbi.1003649. eCollection 2014 Jun.

DOI:10.1371/journal.pcbi.1003649
PMID:24922281
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4055573/
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.

摘要

种间杂交以及随后进化力量的影响所产生的一个结果是基因渗入,即一个物种的遗传物质整合到另一个物种个体的基因组中。几组真核生物物种的进化都涉及杂交,并且通过基因渗入实现适应的例子已经得到证实。在这项工作中,我们报告了PhyloNet - HMM——一种用于检测基因组中基因渗入的新的比较基因组框架。PhyloNet - HMM将系统发育网络与隐马尔可夫模型(HMM)相结合,以同时捕捉基因组的(潜在网状的)进化历史以及基因组内的依赖性。我们工作的一个新颖之处在于它还考虑了不完全谱系分选和位点间的依赖性。将我们的模型应用于小家鼠基因组7号染色体的变异数据,除了其他新检测到的渗入基因组区域外,还检测到了最近报道的涉及啮齿动物抗毒基因Vkorc1的适应性基因渗入事件。根据我们的分析,估计7号染色体上所有位点中约9%是渗入起源的(这些位点覆盖了7号染色体约13兆碱基对以及300多个基因)。此外,我们的模型在一个阴性对照数据集中未检测到基因渗入。我们还发现,我们的模型能够从在具有重组、隔离和迁移的合并模型下模拟的合成数据集中准确检测到基因渗入和其他进化过程。我们的工作为系统分析基因渗入提供了一个强大的框架,同时考虑了位点间的依赖性、点突变、重组和祖先多态性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/492ab45638d8/pcbi.1003649.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/bf6733d3d6e6/pcbi.1003649.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/8271b4da7da6/pcbi.1003649.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/c929c4bc6b8c/pcbi.1003649.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/2174ec51e7cc/pcbi.1003649.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/1947441bd5d7/pcbi.1003649.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/d88947aa6c27/pcbi.1003649.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/431cf103a499/pcbi.1003649.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/e4690f658f38/pcbi.1003649.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/d17929c8e130/pcbi.1003649.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/7f2026191c3b/pcbi.1003649.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/492ab45638d8/pcbi.1003649.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/bf6733d3d6e6/pcbi.1003649.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/8271b4da7da6/pcbi.1003649.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/c929c4bc6b8c/pcbi.1003649.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/2174ec51e7cc/pcbi.1003649.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/1947441bd5d7/pcbi.1003649.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/d88947aa6c27/pcbi.1003649.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/431cf103a499/pcbi.1003649.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/e4690f658f38/pcbi.1003649.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/d17929c8e130/pcbi.1003649.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/7f2026191c3b/pcbi.1003649.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d0/4055573/492ab45638d8/pcbi.1003649.g011.jpg

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