Human Genetics

, Volume 127, Issue 6, pp 651–658 | Cite as

Comparison of the DNA methylation profiles of human peripheral blood cells and transformed B-lymphocytes

  • Yan V. Sun
  • Stephen T. Turner
  • Jennifer A. Smith
  • Pamela I. Hammond
  • Alicia Lazarus
  • Jodie L. Van De Rostyne
  • Julie M. Cunningham
  • Sharon L. R. Kardia
Original Investigation


Epidemiological studies of DNA methylation (DNAm) profiles may hold substantial promise for identifying mechanisms through which genetic and environmental factors jointly contribute to disease risk. Different cell types are likely to have different DNAm patterns. We investigate the DNAm differences between two types of biospecimens available in many genetic epidemiology studies. We compared DNAm patterns in two different DNA samples from each of 34 participants in the Genetic Epidemiology Network of Arteriopathy study (20 Caucasians and 14 African-Americans). One was extracted from peripheral blood cells (PBC) and the other from transformed B-lymphocytes (TBL). The genome-wide DNAm profiles were compared at over 27,000 genome-wide methylation sites. We found that 26 out of the 34 participants had correlation coefficients higher than 0.9 between methylation profiles of PBC and TBL. Although a high correlation was observed in the DNAm profile between PBC and TBL, we also observed variation across samples from different DNA resources and donors. Using principal component analysis of the DNAm profiles, the two sources of the DNA samples could be accurately predicted. We also identified 3,723 autosomal DNAm sites that had significantly different methylation statuses in PBC compared to TBL (Bonferroni corrected p value <0.05). Both PBC and TBL provide a rich resource for understanding the DNAm profiles in humans participating in epidemiologic studies. While the majority of DNAm findings in PBC and TBL may be consistent, caution must be used when interpreting results because of the possibility of cell type-specific methylation modification.

Supplementary material

439_2010_810_MOESM1_ESM.xls (472 kb)
Supplementary Table 1 (XLS 472 kb)


  1. Bird A (2007) Perceptions of epigenetics. Nature 447:396–398CrossRefPubMedGoogle Scholar
  2. Bjornsson HT, Sigurdsson MI, Fallin MD, Irizarry RA, Aspelund T, Cui H, Yu W, Rongione MA, Ekstrom TJ, Harris TB, Launer LJ, Eiriksdottir G, Leppert MF, Sapienza C, Gudnason V, Feinberg AP (2008) Intra-individual change over time in DNA methylation with familial clustering. JAMA 299:2877–2883CrossRefPubMedGoogle Scholar
  3. Brennan EP, Ehrich M, Brazil DP, Crean JK, Murphy M, Sadlier DM, Martin F, Godson C, McKnight AJ, van den Boom D, Maxwell AP, Savage DA (2009) Comparative analysis of DNA methylation profiles in peripheral blood leukocytes versus lymphoblastoid cell lines. Epigenetics 4:159–164CrossRefPubMedGoogle Scholar
  4. Bruniquel D, Schwartz RH (2003) Selective, stable demethylation of the interleukin-2 gene enhances transcription by an active process. Nat Immunol 4:235–240CrossRefPubMedGoogle Scholar
  5. Christensen BC, Houseman EA, Godleski JJ, Marsit CJ, Longacker JL, Roelofs CR, Karagas MR, Wrensch MR, Yeh RF, Nelson HH, Wiemels JL, Zheng S, Wiencke JK, Bueno R, Sugarbaker DJ, Kelsey KT (2009) Epigenetic profiles distinguish pleural mesothelioma from normal pleura and predict lung asbestos burden and clinical outcome. Cancer Res 69:227–234CrossRefPubMedGoogle Scholar
  6. Chu MW, Siegmund KD, Eckstam CL, Kim JY, Yang AS, Kanel GC, Tavare S, Shibata D (2007) Lack of increases in methylation at three CpG-rich genomic loci in non-mitotic adult tissues during aging. BMC Med Genet 8:50CrossRefPubMedGoogle Scholar
  7. Chu M, Siegmund KD, Hao QL, Crooks GM, Tavare S, Shibata D (2008) Inferring relative numbers of human leucocyte genome replications. Br J Haematol 141:862–871CrossRefPubMedGoogle Scholar
  8. Cotran RS, Kumar V, Collins T, Robbins SL (1999) Pathologic basis of disease. WB Saunders, PhiladelphiaGoogle Scholar
  9. Daniels PR, Kardia SL, Hanis CL, Brown CA, Hutchinson R, Boerwinkle E, Turner ST, Genetic Epidemiology Network of Arteriopathy study (2004) Familial aggregation of hypertension treatment and control in the Genetic Epidemiology Network of Arteriopathy (GENOA) Study. Am J Med 116:676–681CrossRefPubMedGoogle Scholar
  10. Davis CD, Ross SA (2007) Dietary components impact histone modifications and cancer risk. Nutr Rev 65:88–94CrossRefPubMedGoogle Scholar
  11. Dong C, Yoon W, Goldschmidt-Clermont PJ (2002) DNA methylation and atherosclerosis. J Nutr 132:2406S–2409SPubMedGoogle Scholar
  12. Ehrlich M (2003) Expression of various genes is controlled by DNA methylation during mammalian development. J Cell Biochem 88:899–910CrossRefPubMedGoogle Scholar
  13. FBPP Investigators (2002) Multi-center genetic study of hypertension: the family blood pressure program (FBPP). Hypertension 39:3–9Google Scholar
  14. Fitzpatrick DR, Wilson CB (2003) Methylation and demethylation in the regulation of genes, cells, and responses in the immune system. Clin Immunol 109:37–45CrossRefPubMedGoogle Scholar
  15. Fitzpatrick DR, Shirley KM, McDonald LE, Bielefeldt-Ohmann H, Kay GF, Kelso A (1998) Distinct methylation of the interferon gamma (IFN-Gamma) and Interleukin 3 (IL-3) genes in newly activated primary CD8+ T lymphocytes: regional IFN-Gamma promoter demethylation and mRNA expression are heritable in CD44(High)CD8+ T cells. J Exp Med 188:103–117CrossRefPubMedGoogle Scholar
  16. Foley DL, Craig JM, Morley R, Olsson CA, Dwyer T, Smith K, Saffery R (2009) Prospects for epigenetic epidemiology. Am J Epidemiol 169:389–400CrossRefPubMedGoogle Scholar
  17. Fridman AL, Tainsky MA (2008) Critical pathways in cellular senescence and immortalization revealed by gene expression profiling. Oncogene 27:5975–5987CrossRefPubMedGoogle Scholar
  18. Herceg Z (2007) Epigenetics and cancer: towards an evaluation of the impact of environmental and dietary factors. Mutagenesis 22:91–103CrossRefPubMedGoogle Scholar
  19. Illumina (2009) Illumina GenomeStudio methylation module v1.0 user guide, Part # 11319130Google Scholar
  20. Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P, Cui H, Gabo K, Rongione M, Webster M, Ji H, Potash JB, Sabunciyan S, Feinberg AP (2009) The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet 41:178–186CrossRefPubMedGoogle Scholar
  21. Kauermann G, Carroll RJ (2001) A note on the efficiency of sandwich covariance matrix estimation. J Am Stat Assoc 96:1387–1396CrossRefGoogle Scholar
  22. Kochanek S, Radbruch A, Tesch H, Renz D, Doerfler W (1991) DNA methylation profiles in the human genes for tumor necrosis factors alpha and beta in subpopulations of leukocytes and in leukemias. Proc Natl Acad Sci USA 88:5759–5763CrossRefPubMedGoogle Scholar
  23. Ladd-Acosta C, Pevsner J, Sabunciyan S, Yolken RH, Webster MJ, Dinkins T, Callinan PA, Fan JB, Potash JB, Feinberg AP (2007) DNA methylation signatures within the human brain. Am J Hum Genet 81:1304–1315CrossRefPubMedGoogle Scholar
  24. Liang KY, Zeger SL (1986) Longitudinal data analysis using generalized linear models. Biometrika 73:13–22CrossRefGoogle Scholar
  25. Lu B, Preisser JS, Qaqish BF, Suchindran C, Bangdiwala SI, Wolfson M (2007) A comparison of two bias-corrected covariance estimators for generalized estimating equations. Biometrics 63:935–941CrossRefPubMedGoogle Scholar
  26. Meissner A, Mikkelsen TS, Gu H, Wernig M, Hanna J, Sivachenko A, Zhang X, Bernstein BE, Nusbaum C, Jaffe DB, Gnirke A, Jaenisch R, Lander ES (2008) Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454:766–770PubMedGoogle Scholar
  27. Metivier R, Gallais R, Tiffoche C, Le Peron C, Jurkowska RZ, Carmouche RP, Ibberson D, Barath P, Demay F, Reid G, Benes V, Jeltsch A, Gannon F, Salbert G (2008) Cyclical DNA methylation of a transcriptionally active promoter. Nature 452:45–50CrossRefPubMedGoogle Scholar
  28. Moverare-Skrtic S, Mellstrom D, Vandenput L, Ehrich M, Ohlsson C (2009) Peripheral blood leukocyte distribution and body mass index are associated with the methylation pattern of the androgen receptor promoter. Endocrine 35:204–210CrossRefPubMedGoogle Scholar
  29. Namihira M, Kohyama J, Abematsu M, Nakashima K (2008) Epigenetic mechanisms regulating fate specification of neural stem cells. Philos Trans R Soc Lond B Biol Sci 363:2099–2109CrossRefPubMedGoogle Scholar
  30. Northrop JK, Thomas RM, Wells AD, Shen H (2006) Epigenetic remodeling of the IL-2 and IFN-Gamma loci in memory CD8 T cells is influenced by CD4 T cells. J Immunol 177:1062–1069PubMedGoogle Scholar
  31. O’Brien LM, Fitzmaurice GM (2004) Analysis of longitudinal multiple-source binary data using generalized estimating equations. J R Stat Soc Ser C Appl Stat 53:177–193CrossRefGoogle Scholar
  32. Pearce EL, Shen H (2006) Making sense of inflammation, epigenetics, and memory CD8+ T-cell differentiation in the context of infection. Immunol Rev 211:197–202CrossRefPubMedGoogle Scholar
  33. Reimand J, Kull M, Peterson H, Hansen J, Vilo J (2007) g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res 35:W193–W200CrossRefPubMedGoogle Scholar
  34. Richards EJ (2006) Inherited epigenetic variation—revisiting soft inheritance. Nat Rev Genet 7:395–401CrossRefPubMedGoogle Scholar
  35. Suzuki MM, Bird A (2008) DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet 9:465–476CrossRefPubMedGoogle Scholar
  36. Wilson AG (2008) Epigenetic regulation of gene expression in the inflammatory response and relevance to common diseases. J Periodontol 79:1514–1519CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Yan V. Sun
    • 1
  • Stephen T. Turner
    • 2
  • Jennifer A. Smith
    • 1
  • Pamela I. Hammond
    • 2
  • Alicia Lazarus
    • 1
  • Jodie L. Van De Rostyne
    • 2
  • Julie M. Cunningham
    • 3
  • Sharon L. R. Kardia
    • 1
  1. 1.Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborUSA
  2. 2.Division of Nephrology and HypertensionMayo ClinicRochesterUSA
  3. 3.Division of Experimental PathologyMayo ClinicRochesterUSA

Personalised recommendations