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Research Article

The Correlation Of Methylation Levels Measured Using Illumina 450K And EPIC Beadchips In Blood Samples

, , , , , , , & show all
Pages 1363-1371 | Received 23 Jun 2017, Accepted 02 Aug 2017, Published online: 15 Aug 2017

References

  • 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 ).
  • 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 ).
  • Kling T , WengerA , BeckS , CarenH . Validation of the MethylationEPIC BeadChip for fresh-frozen and formalin-fixed paraffin-embedded tumours . Clin. Epigenetics9 , 33 ( 2017 ).
  • Sadeh N , SpielbergJM , LogueMWet al. SKA2 methylation is associated with decreased prefrontal cortical thickness and greater PTSD severity among trauma-exposed veterans . Mol. Psychiatry21 ( 3 ), 357 – 363 ( 2016 ).
  • Ratanatharathorn A , BoksM , AielloAet al. Epigenome-wide association of PTSD from heterogeneous cohorts with a common multi-site analysis pipeline . Am. J. Med. Genet. B Neuropsychiatr. Genet. doi:10.1002/ajmg.b.32568 ( 2017 ) ( Epub ahead of print ).
  • R Development Core Team . R: a language and environment for statistical computing . ( 2008 ). www.r-project.org/
  • Chen YA , LemireM , ChoufaniSet al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray . Epigenetics8 ( 2 ), 203 – 209 ( 2013 ).
  • Teschendorff AE , MarabitaF , LechnerMet al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450k DNA methylation data . Bioinformatics29 ( 2 ), 189 – 196 ( 2013 ).
  • Touleimat N , TostJ . Complete pipeline for Infinium(®) Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation . Epigenomics4 ( 3 ), 325 – 341 ( 2012 ).
  • Pidsley R , Y WongCC , VoltaM , LunnonK , MillJ , SchalkwykLC . A data-driven approach to preprocessing Illumina 450K methylation array data . BMC Genomics14 , 293 ( 2013 ).
  • Johnson WE , LiC , RabinovicA . Adjusting batch effects in microarray expression data using empirical Bayes methods . Biostatistics8 ( 1 ), 118 – 127 ( 2007 ).
  • Leek JT , JohnsonWE , ParkerHS , JaffeAE , StoreyJD . sva: Surrogate Variable Analysis . R package version 3.10.0 .
  • Jaffe AE , IrizarryRA . Accounting for cellular heterogeneity is critical in epigenome-wide association studies . Genome Biol.15 ( 2 ), R31 ( 2014 ).
  • Ligthart S , MarziC , AslibekyanSet al. DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases . Genome Biol.17 ( 1 ), 255 ( 2016 ).
  • Horvath S . DNA methylation age of human tissues and cell types . Genome Biol.14 ( 10 ), R115 ( 2013 ).
  • Hannum G , GuinneyJ , ZhaoLet al. Genome-wide methylation profiles reveal quantitative views of human aging rates . Mol. Cell49 ( 2 ), 359 – 367 ( 2013 ).
  • Kanyongo GY , BrookGP , Kyei-BlanksonL , GocmenG . Reliability and statistical power: how measurement fallibility affects power and required sample sizes for several parametric and nonparametric statistics . J. Modern Appl. Stat. Methods6 ( 1 ), 81 – 90 ( 2007 ).
  • Cleary TA , LinnRL , WalsterGW . Effect of reliability and validity on power of statistical tests . Sociol. Methodol.2 , 130 – 138 ( 1970 ).
  • Meng H , JoyceAR , AdkinsDEet al. A statistical method for excluding non-variable CpG sites in high-throughput DNA methylation profiling . BMC Bioinformatics11 , 227 ( 2010 ).
  • Bibikova M , LinZ , ZhouLet al. High-throughput DNA methylation profiling using universal bead arrays . Genome Res.16 ( 3 ), 383 – 393 ( 2006 ).
  • Bjornsson HT , BrownLJ , FallinMDet al. Epigenetic specificity of loss of imprinting of the IGF2 gene in Wilms tumors . J. Natl Cancer Inst.99 ( 16 ), 1270 – 1273 ( 2007 ).
  • Espinal AC , WangD , YanLet al. A methodological study of genome-wide DNA methylation analyses using matched archival formalin-fixed paraffin embedded and fresh frozen breast tumors . Oncotarget8 ( 9 ), 14821 – 14829 ( 2017 ).
  • Wahl S , DrongA , LehneBet al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity . Nature541 ( 7635 ), 81 – 86 ( 2017 ).
  • Lee MK , HongY , KimSY , KimWJ , LondonSJ . Epigenome-wide association study of chronic obstructive pulmonary disease and lung function in Koreans . Epigenomics9 ( 7 ), 971 – 984 ( 2017 ).
  • Rask-Andersen M , MartinssonD , AhsanMet al. Epigenome-wide association study reveals differential DNA methylation in individuals with a history of myocardial infarction . Hum. Mol. Genet.25 ( 21 ), 4739 – 4748 ( 2016 ).
  • Montano C , TaubMA , JaffeAet al. Association of DNA methylation differences with schizophrenia in an epigenome-wide association study . JAMA Psychiatry73 ( 5 ), 506 – 514 ( 2016 ).
  • Shimada-Sugimoto M , OtowaT , MiyagawaTet al. Epigenome-wide association study of DNA methylation in panic disorder . Clin. Epigenetics9 , 6 ( 2017 ).
  • Chen J , JustAC , SchwartzJet al. CpGFilter: model-based CpG probe filtering with replicates for epigenome-wide association studies . Bioinformatics32 ( 3 ), 469 – 471 ( 2016 ).