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DOI | 10.1371/journal.pone.0142274 |
Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach | |
Haque, M. Muksitul1,2; Holder, Lawrence B.2; Skinner, Michael K.1 | |
通讯作者 | Skinner, Michael K. |
来源期刊 | PLOS ONE
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ISSN | 1932-6203 |
出版年 | 2015 |
卷号 | 10期号:11 |
英文摘要 | Environmentally induced epigenetic transgenerational inheritance of disease and phenotypic variation involves germline transmitted epimutations. The primary epimutations identified involve altered differential DNA methylation regions (DMRs). Different environmental toxicants have been shown to promote exposure (i.e., toxicant) specific signatures of germline epimutations. Analysis of genomic features associated with these epimutations identified low-density CpG regions (<3 CpG / 100bp) termed CpG deserts and a number of unique DNA sequence motifs. The rat genome was annotated for these and additional relevant features. The objective of the current study was to use a machine learning computational approach to predict all potential epimutations in the genome. A number of previously identified sperm epimutations were used as training sets. A novel machine learning approach using a sequential combination of Active Learning and Imbalance Class Learner analysis was developed. The transgenerational sperm epimutation analysis identified approximately 50K individual sites with a 1 kb mean size and 3,233 regions that had a minimum of three adjacent sites with a mean size of 3.5 kb. A select number of the most relevant genomic features were identified with the low density CpG deserts being a critical genomic feature of the features selected. A similar independent analysis with transgenerational somatic cell epimutation training sets identified a smaller number of 1,503 regions of genome-wide predicted sites and differences in genomic feature contributions. The predicted genome-wide germline (sperm) epimutations were found to be distinct from the predicted somatic cell epimutations. Validation of the genome-wide germline predicted sites used two recently identified transgenerational sperm epimutation signature sets from the pesticides dichlorodiphenyltrichloroethane (DDT) and methoxychlor (MXC) exposure lineage F3 generation. Analysis of this positive validation data set showed a 100% prediction accuracy for all the DDT-MXC sperm epimutations. Observations further elucidate the genomic features associated with transgenerational germline epimutations and identify a genome-wide set of potential epimutations that can be used to facilitate identification of epigenetic diagnostics for ancestral environmental exposures and disease susceptibility. |
类型 | Article |
语种 | 英语 |
国家 | USA |
收录类别 | SCI-E |
WOS记录号 | WOS:000365070700027 |
WOS关键词 | SPERM EPIMUTATIONS ; PRENATAL EXPOSURE ; DNA METHYLATION ; NONCODING RNAS ; DUTCH FAMINE ; REGIONS ; TRANSCRIPTION ; RESTRICTION ; CLASSIFIERS ; ALGORITHMS |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/189863 |
作者单位 | 1.Washington State Univ, Ctr Reprod Biol, Sch Biol Sci, Pullman, WA 99164 USA; 2.Washington State Univ, Sch Elect Engn & Comp Sci, Pullman, WA 99164 USA |
推荐引用方式 GB/T 7714 | Haque, M. Muksitul,Holder, Lawrence B.,Skinner, Michael K.. Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach[J],2015,10(11). |
APA | Haque, M. Muksitul,Holder, Lawrence B.,&Skinner, Michael K..(2015).Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach.PLOS ONE,10(11). |
MLA | Haque, M. Muksitul,et al."Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach".PLOS ONE 10.11(2015). |
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