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CC BY V4.0, Open Access
Journal article
Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach
PloS one, Vol.10(11), pp.e0142274-e0142274
2015
Handle:
https://hdl.handle.net/2376/110589
PMCID: PMC4646459
PMID: 26571271
Abstract
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.
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Details
- Title
- Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach
- Creators
- M Muksitul Haque - School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, 99164, United States of AmericaLawrence B Holder - School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, 99164, United States of AmericaMichael K Skinner - Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, Washington, 99164-4236, United States of America
- Publication Details
- PloS one, Vol.10(11), pp.e0142274-e0142274
- Academic Unit
- Biological Sciences, School of; Electrical Engineering and Computer Science, School of
- Publisher
- United States
- Grant note
- R01 ES012974-10 / NIEHS NIH HHS R01 ES012974 / NIEHS NIH HHS
- Identifiers
- 99900547171801842
- Language
- English
- Resource Type
- Journal article