CUE: CpG impUtation ensemble for DNA methylation levels across the human methylation450 (HM450) and EPIC (HM850) BeadChip platforms

References

• Iurlaro M, von Meyenn F, Reik W. DNA methylation homeostasis in human and mouse development. Curr Opin Genet Dev. 2017;43:101–109.
   Google Scholar
• Klutstein M, Nejman D, Greenfield R, et al. DNA methylation in cancer and aging. Cancer Res. 2016;76:3446–3450.
   Google Scholar
• Joubert BR, Felix JF, Yousefi P, et al. DNA methylation in newborns and maternal smoking in pregnancy: genome-wide consortium meta-analysis. Am J Hum Genet. 2016;98:680–696.
   Google Scholar
• Bird A. DNA methylation patterns and epigenetic memory. Genes Dev. 2002;16:6–21.
   Google Scholar
• Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19:371.
   Google Scholar
• Turecki G, Meaney MJ. Effects of the social environment and stress on glucocorticoid receptor gene methylation: a systematic review. Biol Psychiatry. 2016;79:87–96.
   Google Scholar
• Gonzalo S. Epigenetic alterations in aging. J Appl Physiol. 2010;109:586–597.
   Google Scholar
• Bakusic J, Schaufeli W, Claes S, et al. Stress, burnout and depression: A systematic review on DNA methylation mechanisms. J Psychosom Res. 2017;92:34–44.
   Google Scholar
• Bibikova M, Barnes B, Tsan C, et al. High density DNA methylation array with single CpG site resolution. Genomics. 2011;98:288–295.
   Google Scholar
• Dedeurwaerder S, Defrance M, Bizet M, et al. A comprehensive overview of Infinium HumanMethylation450 data processing. Brief Bioinform. 2013;15:929–941.
   Google Scholar
• Bibikova M, Le J, Barnes B, et al. Genome-wide DNA methylation profiling using Infinium® assay. Epigenomics. 2009;1:177–200.
   Google Scholar
• Moran S, Arribas C, Esteller M. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics. 2016;8:389–399.
   Google Scholar
• Das D, Ghosh S, Maitra A, et al. Epigenomic dysregulation-mediated alterations of key biological pathways and tumor immune evasion are hallmarks of gingivo-buccal oral cancer. Clin Epigenetics. 2019;11:1–12.
   Google Scholar
• Pidsley R, Zotenko E, Peters TJ, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17:208.
   Google Scholar
• Zhou W, Jr TJ T, Laird PW, et al. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 2018;46:e123–e.
   Google Scholar
• Consortium EP. The ENCODE (ENCyclopedia of DNA elements) project. Science. 2004;306:636–640.
   Google Scholar
• Li Y, Willer C, Sanna S, et al. Genotype imputation. Annu Rev Genomics Hum Genet. 2009;10:387–406.
   Google Scholar
• Das S, Forer L, Schönherr S, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–1287.
   Google Scholar
• Liu EY, Li M, Wang W, et al. MaCH‐Admix: genotype imputation for admixed populations. Genet Epidemiol. 2013;37:25–37.
   Google Scholar
• Bhasin M, Zhang H, Reinherz EL, et al. Prediction of methylated CpGs in DNA sequences using a support vector machine. FEBS Lett. 2005;579:4302–4308.
   Google Scholar
• Bock C, Paulsen M, Tierling S, et al. CpG island methylation in human lymphocytes is highly correlated with DNA sequence, repeats, and predicted DNA structure. PLoS Genet. 2006;2:e26.
   Google Scholar
• Das R, Dimitrova N, Xuan Z, et al. Computational prediction of methylation status in human genomic sequences. Proc Nat Acad Sci. 2006;103:10713–10716.
   Google Scholar
• Fang F, Fan S, Zhang X, et al. Predicting methylation status of CpG islands in the human brain. Bioinformatics. 2006;22:2204–2209.
   Google Scholar
• Liu Z, Xiao X, Qiu W-R, et al. iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Anal Biochem. 2015;474:69–77.
   Google Scholar
• Ma B, Wilker EH, Willis-Owen SA, et al. DNA methylation level across human tissues. Nucleic Acids Res. 2014;42:3515–3528.
   Google Scholar
• Zhou X, Li Z, Dai Z, et al. Prediction of methylation CpGs and their methylation degrees in human DNA sequences. Comput Biol Med. 2012;42:408–413.
   Google Scholar
• Zhou X, Tuck DP. MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data. Bioinformatics. 2007;23:1106–1114.
   Google Scholar
• Zhang W, Spector TD, Deloukas P, et al. Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements. Genome Biol. 2015;16:14.
   Google Scholar
• Bengio Y. Learning deep architectures for AI. Found Trends® Mach Learn. 2009;2:1–127.
   Google Scholar
• Angermueller C, Lee HJ, Reik W, et al. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 2017;18:67.
   Google Scholar
• Zou LS, Erdos MR, Taylor DL, et al. BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues. bioRxiv. 2018;19:390.
   Google Scholar
• Zhang G, Huang KC, Xu Z, et al. Across‐platform imputation of DNA methylation levels incorporating nonlocal information using penalized functional regression. Genet Epidemiol. 2016;40:333–340.
   Google Scholar
• Goldsmith J, Bobb J, Crainiceanu CM, et al. Penalized functional regression. J Comput Graph Stat. 2011;20:830–851.
   Google Scholar
• Logue MW, Smith AK, Wolf EJ, et al. The correlation of methylation levels measured using Illumina 450K and EPIC BeadChips in blood samples. Epigenomics. 2017;9:1363–1371.
   Google Scholar
• Santos HP Jr., Bhattacharya A, Martin EM, et al. Epigenome-wide DNA methylation in placentas from preterm infants: association with maternal socioeconomic status. Epigenetics. 2019;14:751–765.
   Google Scholar
• Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–127.
   Google Scholar
• Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115.
   Google Scholar
• Ratanatharathorn A, Boks MP, Maihofer AX, et al. Epigenome‐wide association of PTSD from heterogeneous cohorts with a common multi‐site analysis pipeline. Am J Med Genet B Neuropsychiatr Genet. 2017;174:619–630.
   Google Scholar
• Team RDC. R: A language and environment for statistical computing; 2008. Available from: www.r-project.org/.
   Google Scholar
• Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30:1363–1369.
   Google Scholar
• Addo KA, Bulka C, Dhingra R, et al. Acetaminophen use during pregnancy and DNA methylation in the placenta of the extremely low gestational age newborn (ELGAN) cohort. Environ Epigenet. 2019;5:dvz010.
   Google Scholar
• Bulka CM, Dammann O, Santos HP, et al. Placental CpG methylation of inflammation, angiogenic, and neurotrophic genes and retinopathy of prematurity. Invest Ophthalmol Vis Sci. 2019;60:2888–2894.
   Google Scholar
• Clark J, Martin E, Bulka CM, et al. Associations between placental CpG methylation of metastable epialleles and childhood body mass index across ages one, two and ten in the Extremely Low Gestational Age Newborns (ELGAN) cohort. Epigenetics. 2019;14:1102–1111.
   Google Scholar
• Bø TH, Dysvik B, Jonassen I. LSimpute: accurate estimation of missing values in microarray data with least squares methods. Nucleic Acids Res. 2004;32:e34–e.
   Google Scholar
• Kim H, Golub GH, Park H. Missing value estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics. 2006;22:1410–1411.
   Google Scholar
• Liew AW-C, Law N-F YH. Missing value imputation for gene expression data: computational techniques to recover missing data from available information. Brief Bioinform. 2010;12:498–513.
   Google Scholar
• Troyanskaya O, Cantor M, Sherlock G, et al. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001;17:520–525.
   Google Scholar
• Network CGA. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490:61.
   Google Scholar
• Network CGA. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature. 2015;517:576.
   Google Scholar
• Fan R, Wang Y, Boehnke M, et al. Gene level meta-analysis of quantitative traits by functional linear models. Genetics. 2015;115:178343.
   Google Scholar
• Fan R, Wang Y, Chiu C-Y, et al. Meta-analysis of complex diseases at gene level by generalized functional linear models. Genetics. 2015;115:180869.
   Google Scholar