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Research Article

Positional Effects Revealed in Illumina Methylation Array and The Impact on Analysis

, , , , , , & show all
Pages 643-659 | Received 27 Aug 2017, Accepted 17 Jan 2018, Published online: 22 Feb 2018

References

  • Jaenisch R , BirdA . Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals . Nat. Genet.33 ( Suppl. ), 245 – 254 ( 2003 ).
  • Jones PA . Functions of DNA methylation: islands, start sites, gene bodies and beyond . Nat. Rev. Genet.13 ( 7 ), 484 – 492 ( 2012 ).
  • Girardot M , FeilR , LleresD . Epigenetic deregulation of genomic imprinting in humans: causal mechanisms and clinical implications . Epigenomics5 ( 6 ), 715 – 728 ( 2013 ).
  • Mccabe DC , CaudillMA . DNA methylation, genomic silencing, and links to nutrition and cancer . Nutr. Rev.63 ( 6 Pt 1 ), 183 – 195 ( 2005 ).
  • Heyn H , VidalE , FerreiraHJet al. Epigenomic analysis detects aberrant super-enhancer DNA methylation in human cancer . Genome Biol.17 ( 1 ), 11 ( 2016 ).
  • Boerno ST , GrimmC , LehrachH , SchweigerMR . Next-generation sequencing technologies for DNA methylation analyses in cancer genomics . Epigenomics2 ( 2 ), 199 – 207 ( 2010 ).
  • Robertson KD . DNA methylation and human disease . Nat. Rev. Genet.6 ( 8 ), 597 – 610 ( 2005 ).
  • Pogribny IP , BelandFA . DNA hypomethylation in the origin and pathogenesis of human diseases . Cell Mol. Life Sci.66 ( 14 ), 2249 – 2261 ( 2009 ).
  • Wilson AS , PowerBE , MolloyPL . DNA hypomethylation and human diseases . Biochim. Biophys. Acta1775 ( 1 ), 138 – 162 ( 2007 ).
  • Heyn H , LiN , FerreiraHJet al. Distinct DNA methylomes of newborns and centenarians . Proc. Natl Acad. Sci. USA109 ( 26 ), 10522 – 10527 ( 2012 ).
  • Spiers H , HannonE , SchalkwykLCet al. Methylomic trajectories across human fetal brain development . Genome Res.25 ( 3 ), 338 – 352 ( 2015 ).
  • Lim AS , SrivastavaGP , YuLet al. 24-hour rhythms of DNA methylation and their relation with rhythms of RNA expression in the human dorsolateral prefrontal cortex . PLoS Genet.10 ( 11 ), e1004792 ( 2014 ).
  • Muangsub T , SamsuwanJ , TongyooP , KitkumthornN , MutiranguraA . Analysis of methylation microarray for tissue specific detection . Gene553 ( 1 ), 31 – 41 ( 2014 ).
  • Bibikova M , LeJ , BarnesBet al. Genome-wide DNA methylation profiling using Infinium® assay . Epigenomics1 ( 1 ), 177 – 200 ( 2009 ).
  • Dedeurwaerder S , DefranceM , CalonneE , DenisH , SotiriouC , FuksF . Evaluation of the Infinium Methylation 450 K technology . Epigenomics3 ( 6 ), 771 – 784 ( 2011 ).
  • Dedeurwaerder S , DefranceM , BizetM , CalonneE , BontempiG , FuksF . A comprehensive overview of Infinium HumanMethylation450 data processing . Brief. Bioinform.15 ( 6 ), 929 – 941 ( 2014 ).
  • Moran S , ArribasC , EstellerM . Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences . Epigenomics8 ( 3 ), 389 – 399 ( 2016 ).
  • Bibikova M . High-throughput DNA methylation profiling using universal bead arrays . Genome Research16 ( 3 ), 383 – 393 ( 2006 ).
  • Teh AL , PanH , LinXet al. Comparison of methyl-capture sequencing vs Infinium 450 K methylation array for methylome analysis in clinical samples . Epigenetics11 ( 1 ), 36 – 48 ( 2016 ).
  • Bibikova M , BarnesB , TsanCet al. High density DNA methylation array with single CpG site resolution . Genomics98 ( 4 ), 288 – 295 ( 2011 ).
  • Naeem H , WongNC , ChattertonZet al. Reducing the risk of false discovery enabling identification of biologically significant genome-wide methylation status using the HumanMethylation450 array . BMC Genomics15 , 51 ( 2014 ).
  • Teschendorff AE , MarabitaF , LechnerMet al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 K DNA methylation data . Bioinformatics29 ( 2 ), 189 – 196 ( 2013 ).
  • Chen YA , LemireM , ChoufaniSet al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray . Epigenetics8 ( 2 ), 203 – 209 ( 2013 ).
  • Leek JT , ScharpfRB , BravoHCet al. Tackling the widespread and critical impact of batch effects in high-throughput data . Nat. Rev. Genet.11 ( 10 ), 733 – 739 ( 2010 ).
  • Cazaly E , ThomsonR , MarthickJR , HollowayAF , CharlesworthJ , DickinsonJL . Comparison of pre-processing methodologies for Illumina 450 K methylation array data in familial analyses . Clin. Epigenetics8 , 75 ( 2016 ).
  • Johnson WE , LiC , RabinovicA . Adjusting batch effects in microarray expression data using empirical Bayes methods . Biostatistics8 ( 1 ), 118 – 127 ( 2007 ).
  • Sun Z , ChaiHS , WuYet al. Batch effect correction for genome-wide methylation data with Illumina Infinium platform . BMC Med. Genomics4 , 84 ( 2011 ).
  • Chen C , GrennanK , BadnerJet al. Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods . PLoS ONE6 ( 2 ), e17238 ( 2011 ).
  • Lazar C , MeganckS , TaminauJet al. Batch effect removal methods for microarray gene expression data integration: a survey . Brief. Bioinform.14 ( 4 ), 469 – 490 ( 2013 ).
  • Maksimovic J , Gagnon-BartschJA , SpeedTP , OshlackA . Removing unwanted variation in a differential methylation analysis of Illumina HumanMethylation450 array data . Nucleic Acids Res.43 ( 16 ), e106 ( 2015 ).
  • Akulenko R , MerlM , HelmsV . BEclear: batch effect detection and adjustment in DNA methylation data . PLoS ONE11 ( 8 ), e0159921 ( 2016 ).
  • Buhule OD , MinsterRL , HawleyNLet al. Stratified randomization controls better for batch effects in 450 K methylation analysis: a cautionary tale . Front. Genet.5 , 354 ( 2014 ).
  • Verdugo RA , DeschepperCF , MunozG , PompD , ChurchillGA . Importance of randomization in microarray experimental designs with Illumina platforms . Nucleic Acids Res.37 ( 17 ), 5610 – 5618 ( 2009 ).
  • Teschendorff AE , ZhuangJ , WidschwendterM . Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies . Bioinformatics27 ( 11 ), 1496 – 1505 ( 2011 ).
  • Moran S , VizosoM , Martinez-CardusAet al. Validation of DNA methylation profiling in formalin-fixed paraffin-embedded samples using the Infinium HumanMethylation450 microarray . Epigenetics9 ( 6 ), 829 – 833 ( 2014 ).
  • Kulkarni H , KosMZ , NearyJet al. Novel epigenetic determinants of Type 2 diabetes in Mexican–American families . Hum. Mol. Genet.24 ( 18 ), 5330 – 5344 ( 2015 ).
  • Fortin JP , LabbeA , LemireMet al. Functional normalization of 450 K methylation array data improves replication in large cancer studies . Genome Biol.15 ( 12 ), 503 ( 2014 ).
  • Barrett T , TroupDB , WilhiteSEet al. NCBI GEO: archive for high-throughput functional genomic data . Nucleic Acids Res.37 , D885 – D890 ( 2009 ).
  • De Jager PL , SrivastavaG , LunnonKet al. Alzheimer’s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci . Nat. Neurosci.17 ( 9 ), 1156 – 1163 ( 2014 ).
  • Bennett DA , SchneiderJA , ArvanitakisZ , WilsonRS . Overview and findings from the religious orders study . Curr. Alzheimer Res.9 ( 6 ), 628 – 645 ( 2012 ).
  • Jaffe AE , GaoY , Deep-SoboslayAet al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex . Nat. Neurosci.19 ( 1 ), 40 – 47 ( 2016 ).
  • Zhang D , ChengL , BadnerJAet al. Genetic control of individual differences in gene-specific methylation in human brain . Am. J. Hum. Genet.86 ( 3 ), 411 – 419 ( 2010 ).
  • Numata S , YeT , HydeTMet al. DNA methylation signatures in development and aging of the human prefrontal cortex . Am. J. Hum. Genet.90 ( 2 ), 260 – 272 ( 2012 ).
  • Bell JT , PaiAA , PickrellJKet al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines . Genome Biol.12 ( 1 ), R10 ( 2011 ).
  • Pidsley R , ZotenkoE , PetersTJet al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling . Genome Biol.17 ( 1 ), 208 ( 2016 ).
  • Brain Cloud . http://braincloud.jhmi.edu/downloads.htm
  • Team RC . R: A language and environment for statistical computing . www.gbif.org/tool/81287/r-a-language-and-environment-for-statistical-computing
  • The R Project for Statistical Computing . www.r-project.org/
  • Troyanskaya O , CantorM , SherlockGet al. Missing value estimation methods for DNA microarrays . Bioinformatics17 ( 6 ), 520 – 525 ( 2001 ).
  • 1000 Genomes Project Consortium , AbecasisGR , AltshulerDet al. A map of human genome variation from population-scale sequencing . Nature467 ( 7319 ), 1061 – 1073 ( 2010 ).
  • Houseman EA , KelseyKT , WienckeJK , MarsitCJ . Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective . BMC Bioinformatics16 , 95 ( 2015 ).
  • Leek JT , JohnsonWE , ParkerHS , JaffeAE , StoreyJD . The sva package for removing batch effects and other unwanted variation in high-throughput experiments . Bioinformatics28 ( 6 ), 882 – 883 ( 2012 ).
  • Aryee MJ , JaffeAE , Corrada-BravoHet al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays . Bioinformatics30 ( 10 ), 1363 – 1369 ( 2014 ).
  • Fortin JP , TricheTJJr , HansenKD . Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi . Bioinformatics33 ( 4 ), 558 – 560 ( 2017 ).
  • Triche TJ Jr , WeisenbergerDJ , Van Den BergD , LairdPW , SiegmundKD . Low-level processing of Illumina Infinium DNA Methylation BeadArrays . Nucleic Acids Res.41 ( 7 ), e90 ( 2013 ).
  • GitHub . https://github.com/ChuanJ/JCPackage
  • Storey JD , TibshiraniR . Statistical significance for genomewide studies . Proc. Natl Acad. Sci. USA100 ( 16 ), 9440 – 9445 ( 2003 ).
  • Bates D , MächlerM , BolkerB , WalkerS . Fitting linear mixed-effects models using lme4 . J. Stat. Software67 ( 1 ), doi:10.18637/jss.v067.i01 ( 2015 ).
  • Ritchie ME , PhipsonB , WuDet al. Limma powers differential expression analyses for RNA-sequencing and microarray studies . Nucleic Acids Res.43 ( 7 ), e47 ( 2015 ).
  • Delong ER , DelongDM , Clarke-PearsonDL . Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach . Biometrics44 ( 3 ), 837 – 845 ( 1988 ).
  • Agha G , HousemanEA , KelseyKT , EatonCB , BukaSL , LoucksEB . Adiposity is associated with DNA methylation profile in adipose tissue . Int. J. Epidemiol.44 ( 4 ), 1277 – 1287 ( 2015 ).
  • Marabita F , AlmgrenM , LindholmMEet al. An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform . Epigenetics8 ( 3 ), 333 – 346 ( 2013 ).
  • Harrison JM , HowardD , MalvenMet al. Principal variance component analysis of crop composition data: a case study on herbicide-tolerant cotton . J. Agric. Food Chem.61 ( 26 ), 6412 – 6422 ( 2013 ).
  • Boedigheimer MJ , WolfingerRD , BassMBet al. Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories . BMC Genomics9 , 285 ( 2008 ).

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