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Methodology

Increased Correlation Between Methylation Sites In Epigenome-Wide Replication Studies: Impact on Analysis And Results

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Pages 1489-1502 | Received 11 Jul 2017, Accepted 11 Aug 2017, Published online: 06 Nov 2017

References

  • Eckhardt F , LewinJ , CorteseRet al. DNA methylation profiling of human chromosomes 6, 20 and 22 . Nat. Genet.38 ( 12 ), 1378 – 1385 ( 1987 ).
  • Gardiner-Garden M , FrommerM . CpG islands in vertebrate genomes . J. Mol. Biol.196 ( 2 ), 261 – 282 ( 1987 ).
  • Ong ML , HolbrookJD . Novel region discovery method for Infinium 450 K DNA methylation data reveals changes associated with aging in muscle and neuronal pathways . Aging Cell13 ( 1 ), 142 – 155 ( 2014 ).
  • Jaffe AE , MurakamiP , LeeHet al. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies . Int. J. Epidemiol.41 ( 1 ), 200 – 209 ( 2012 ).
  • Sofer T , SchifanoED , HoppinJA , HouL , BaccarelliAA . A-clustering: a novel method for the detection of co-regulated methylation regions, and regions associated with exposure . Bioinformatics29 ( 22 ), 2884 – 2891 ( 2013 ).
  • Langfelder P , HorvathS . WGCNA: an R package for weighted correlation network analysis . BMC Bioinformatics9 , 559 ( 2008 ).
  • Lin X , BartonS , HolbrookJD . How to make DNA methylome wide association studies more powerful . Epigenomics8 ( 8 ), 1117 – 1129 ( 2016 ).
  • Benjamini Y , HochbergY . Controlling the false discovery rate: a practical and powerful approach to multiple testing . J. R. Stat. Soc. Ser. B.57 ( 1 ), 289 – 300 ( 1995 ).
  • Heller R , BogomolovM , BenjaminiY . Deciding whether follow-up studies have replicated findings in a preliminary large-scale omics study . Proc. Natl Acad. Sci. USA111 ( 46 ), 16262 – 16267 ( 2014 ).
  • Sofer T , HellerR , BogomolovMet al. A powerful statistical framework for generalization testing in GWAS, with application to the HCHS/SOL . Genet. Epidemiol.41 , 251 – 258 ( 2017 ).
  • Joubert BR , FelixJF , YousefiPet al. DNA Methylation in newborns and maternal smoking in pregnancy: genome-wide consortium meta-analysis . Am. J. Hum. Genet.98 ( 4 ), 680 – 696 ( 2016 ).
  • Yousefi P , HuenK , DavéV , BarcellosL , EskenaziB , HollandN . Sex differences in DNA methylation assessed by 450 K BeadChip in newborns . BMC Genomics16 , 911 ( 2015 ).
  • Rahmani E , ZaitlenN , BaranYet al. Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies . Nat. Methods13 ( 5 ), 443 – 445 ( 2016 ).
  • Richiardi L , BaussanoI , VizziniLet al. Feasibility of recruiting a birth cohort through the Internet: the experience of the NINFEA cohort . Eur. J. Epidemiol.22 , 831 – 837 ( 2007 ).
  • Du P , ZhangX , HuangC-Cet al. Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis . BMC Bioinformatics11 , 587 ( 2010 ).
  • R Core Team . R: a language and environment for statistical computing . R Foundation for Statistical Computing , Vienna, Austria ( 2017 ). www.R-project.org/ .
  • Fisher RA . Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population . Biometrika10 ( 4 ), 507 – 521 ( 1915 ).
  • Zeileis A . Econometric computing with HC and HAC covariance matrix estimators . J. Stat. Softw.11 ( 10 ), 1 – 17 ( 2004 ).
  • Houseman EA , MolitorJ , MarsitCJ . Reference-free cell mixture adjustments in analysis of DNA methylation data . Bioinformatics30 ( 10 ), 1431 – 1439 ( 2014 ).
  • McGregor K , BernatskyS , ColmegnaIet al. An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies . Genome Biol.17 , 84 ( 2016 ).
  • Massey FJJ . The Kolmogorov–Smirnov test for goodness of fit . J. Am. Stat. Assoc.46 , 68 – 78 ( 1951 ).
  • Razali NM , WahYB . Power comparisons of Shapiro–Wilk, Kolmogorov–Smirnov, Lilliefors and Anderson–Darling tests . J. Stat. Modeling Anal2 , 21 – 33 ( 2011 ).
  • Anderson TW , DarlingDA . A test of goodness of fit . J. Am. Stat. Assoc.49 ( 268 ), 765 – 769 ( 1954 ).
  • Stephens MA . EDF statistics for goodness of fit and some comparisons . J. Am. Stat. Assoc.69 , 730 – 737 ( 1974 ).
  • Warnes GR , BolkerB , LumleyT . gtools: various R programming Tools ( 2015 ). https://CRAN.R-project.org/package=gtools .
  • Komsta L , NovomestkyF . Moments: moments, cumulants, skewness, kurtosis and related tests ( 2015 ). https://CRAN.R-project.org/package=moments .
  • Revolution Analytics , WestonS . Foreach: provides foreach looping construct for R ( 2015 ). https://CRAN.R-project.org/package=foreach .
  • Heller R , BogomolovM , BenjaminiY . Deciding whether follow-up studies have replicated findings in a preliminary large-scale “omics’ study” . www.runmycode.org/companion/view/542 .
  • Lin MF , LucasHC , ShmueliG . Too big to fail: large samples and the p-value problem . Inform. Syst. Res.24 , 906 – 917 ( 2013 ).
  • Morales E , VilahurN , SalasLAet al. Genome-wide DNA methylation study in human placenta identifies novel loci associated with maternal smoking during pregnancy . Int. J. Epidemiol.45 ( 5 ), 1644 – 1655 ( 2016 ).
  • Gruzieva O , XuCJ , BretonCVet al. Epigenome-wide meta-analysis of methylation in children related to prenatal NO2 air pollution exposure . Environ. Health Perspect.125 ( 1 ), 104 – 110 ( 2017 ).
  • Good P . Permutation Tests: A Practical Guide To Resampling Methods For Testing Hypotheses (2nd edition) . Springer-Verlag , NY, USA ( 1994 ).
  • Conneely KN , BoehnkeM . So many correlated tests, so little time! Rapid adjustment of P-values for multiple correlated tests . Am. J. Hum. Genet.81 ( 6 ), 1158 – 1168 ( 2007 ).
  • Nyholt DR . A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other . Am. J. Hum. Genet.74 ( 4 ), 765 – 769 ( 2004 ).
  • Dudbridge F , KoelemanBPC . Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies . Am. J. Hum. Genet.75 ( 3 ), 424 – 435 ( 2004 ).
  • Yekutieli D , BenjaminiY . Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics . J. Stat. Plan. Infer.82 , 171 – 196 ( 1999 ).
  • Benjamini Y , YekutieliD . The control of the false discovery rate in multiple testing under dependency . Ann. Stat.29 ( 4 ), 1165 – 1188 ( 2001 ).
  • Van Iterson M , van ZwetEW , HeijmansBT , BIOS Consortium . Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution . Genome Biol.18 ( 1 ), 19 ( 2017 ).

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