title: Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach.
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.
authors.affiliation: Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, Washington, 99164-4236, United States of America.|School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, 99164, United States of America.
authors.forename: M Muksitul
authors.initials: MM
authors.lastname: Haque
authors.affiliation: School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, 99164, United States of America.
authors.forename: Lawrence B
authors.initials: LB
authors.lastname: Holder
authors.affiliation: Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, Washington, 99164-4236, United States of America.
authors.forename: Michael K
authors.initials: MK
authors.lastname: Skinner
mesh_terms: D001499:Bayes Theorem; D002875:Chromosomes / Q000648:ultrastructure; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D018899:CpG Islands; D003634:DDT / Q000633:toxicity*; D019175:DNA Methylation; D030541:Databases, Genetic; D004781:Environmental Exposure; D044127:Epigenesis, Genetic; D005260:Female; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006107:Granulosa Cells / Q000187:drug effects / Q000378:metabolism; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008731:Methoxychlor / Q000633:toxicity*; D009154:Mutation; D010641:Phenotype; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA; D012708:Sertoli Cells / Q000187:drug effects / Q000378:metabolism; D013094:Spermatozoa / Q000187:drug effects
chemical_list: D003634:DDT; D008731:Methoxychlor