CUE: CpG impUtation ensemble for DNA methylation levels across the human methylation450 (HM450) and EPIC (HM850) BeadChip platforms
Year: 2020
Type: article
Abstract: DNA methylation at CpG dinucleotides is one of the most extensively studied epigenetic marks. With technological advancements, geneticists can profile DNA methylation with multiple reliable approaches. However, profiling platforms can differ substantially in the CpGs they assess, consequently hindering integrated analysis across platforms. Here, we present CpG impUtation Ensemble (CUE), which leverages multiple classical statistical and modern machine learning methods, to impute from the Illumina HumanMethylation450 (HM450) BeadChip to the Illumina HumanMethylationEPIC (HM850) BeadChip. Data were analyzed from two population cohorts with methylation measured both by HM450 and HM850: the Extremely Low Gestational Age Newborns (ELGAN) study ( n =127, placenta) and the VA Boston Posttraumatic Stress Disorder (PTSD) genetics repository ( n =144, whole blood). Cross-validation results show that CUE achieves the lowest predicted root mean square error (RMSE) (0.026 in PTSD) and the highest accuracy (99.97% in PTSD) compared with five individual methods tested, including k-nearest-neighbors, logistic regression, penalized functional regression, random forest and XGBoost. Finally, among all 339,033 HM850-only CpG sites shared between ELGAN and PTSD, CUE successfully imputed 289,604 (85.4%) sites, where success was defined as RMSE < 0.05 and accuracy >95% in PTSD. In summary, CUE is a valuable tool for imputing CpG methylation from the HM450 to HM850 platform.
Source: Epigenetics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Funders: U.S. Department of Veterans Affairs, National Institutes of Health, Korea National Institute of Health, National Heart, Lung, and Blood Institute, National Institute of Nursing Research, National Institute of Neurological Disorders and Stroke, National Institute of Environmental Health Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G1017297203, G1515265980, G2368211124, G2880266453, G3033166606, G3366976786, G3697345281, G4450964518, G4787961582, Genetic Studies of Blood Cell Traits in Multi-Ethnic Cohorts, G4840818279, G6983259670, Neonatal Biomarkers in Extremely Preterm Babies Predict Childhood Brain Disorders, G9000086074, UNC Center for Environmental Health and Susceptibility, The Genetic Epidemiology of Heart, Lung, and Blood Traits Training Grant (GenHLB)
OpenAlex ID: W3090808524