Reliability of DNA methylation measures from dried blood spots and mononuclear cells using the HumanMethylation450k BeadArray

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Dugue, Pierre Antoine ^a,b   English, Dallas R ^a,b   MacInnis, Robert J ^a,b   Jung, Chol Hee ^c   Bassett, Julie K ^a   Fitzgerald, Liesel M ^a   Wong, Ee Ming ^c   Joo, Jihoon E ^c   Hopper, John L ^b   Southey, Melissa C ^c   Giles, Graham G ^a,b   Milne, Roger L ^a,b  

^a CANCER EPIDEMIOLOGY CENTRE   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

^c UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMISTRY; CPG ISLAND; DIAGNOSTIC KIT; DNA METHYLATION; DNA MICROARRAY; DRIED BLOOD SPOT TESTING; HUMAN; METABOLISM; MONOCYTE; PROCEDURES; REPRODUCIBILITY; STANDARDS;

CPG ISLANDS; DNA METHYLATION; DRIED BLOOD SPOT TESTING; HUMANS; MONOCYTES; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REAGENT KITS, DIAGNOSTIC; REPRODUCIBILITY OF RESULTS;

EID: 84979743383     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep30317     Document Type: Article

References (43)

1. Issa, J. P. Aging and epigenetic drift: a vicious cycle. J. Clin. Invest. 124, 24-29 (2014).
2. Robertson, K. D. DNA methylation and human disease. Nat. Rev. Genet. 6, 597-610 (2005).
3. Das, P. M., Singal, R. DNA methylation and cancer. J. Clin. Oncol. 22, 4632-4642 (2004).
4. Yousefi, P. et al. Considerations for normalization of DNA methylation data by Illumina 450K BeadChip assay in population studies. Epigenetics 8, 1141-1152 (2013).
5. Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733-739 (2010).
6. Heijmans, B. T., Mill, J. Commentary: The seven plagues of epigenetic epidemiology. Int. J. Epidemiol. 41, 74-78 (2012).
7. Fortin, J. P. et al. Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol. 15, 503 (2014).
8. Maksimovic, J., Gagnon-Bartsch, J. A., Speed, T. P., Oshlack, A. Removing unwanted variation in a differential methylation analysis of Illumina HumanMethylation450 array data. Nucleic Acids Res. 43, e106 (2015).
9. Wu, M. C. et al. A systematic assessment of normalization approaches for the Infinium 450K methylation platform. Epigenetics 9, 318-329 (2014).
10. Wang, T. et al. A systematic study of normalization methods for Infinium 450K methylation data using whole-genome bisulfite sequencing data. Epigenetics 10, 662-669 (2015).
11. Armstrong, B. K., White, E., Saracci, R. Principles of Exposure Measurement in Epidemiology. (Oxford University Press, 1992).
12. Bibikova, M. et al. High density DNA methylation array with single CpG site resolution. Genomics 98, 288-295 (2011).
13. Sandoval, J. et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6, 692-702 (2011).
14. Dedeurwaerder, S. et al. Evaluation of the Infinium Methylation 450K technology. Epigenomics 3, 771-784 (2011).
15. Bose, M. et al. Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study. BMC Bioinformatics 15, 312 (2014).
16. Shvetsov, Y. B. et al. Intraindividual variation and short-term temporal trend in DNA methylation of human blood. Cancer Epidemiol. Biomarkers Prev. 24, 490-497 (2015).
17. Severi, G. et al. Epigenome-wide methylation in DNA from peripheral blood as a marker of risk for breast cancer. Breast Cancer Res. Treat. 148, 665-673 (2014).
18. Giles, G. G., English, D. R. The Melbourne Collaborative Cohort Study. IARC Sci. Publ. 156, 69-70 (2002).
19. Joo, J. E. et al. The use of DNA from archival dried blood spots with the Infinium HumanMethylation450 array. BMC Biotechnol. 13, 23 (2013).
20. Aryee, M. J. et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30, 1363-1369 (2014).
21. Maksimovic, J., Gordon, L., Oshlack, A. SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol. 13, R44 (2012).
22. Du, P. et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11, 587 (2010).
23. Chen, J. et al. CpGFilter: model-based CpG probe filtering with replicates for epigenome-wide association studies. Bioinformatics 32, 469-471 (2015).
24. Johnson, W. E., Li, C., Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118-127 (2007).
25. de Vet, H. C., Terwee, C. B., Knol, D. L., Bouter, L. M. When to use agreement versus reliability measures. J. Clin. Epidemiol. 59, 1033-1039 (2006).
26. van Veldhoven, K. et al. Epigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis. Clin. Epigenet. 7, 67 (2015).
27. Wong Doo, N. et al. Global measures of peripheral blood-derived DNA methylation as a risk factor in the development of mature B-cell neoplasms. Epigenomics 8, 55-66 (2015).
28. Zeilinger, S. et al. Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS One 8, e63812 (2013).
29. Guida, F. et al. Dynamics of smoking-induced genome-wide methylation changes with time since smoking cessation. Hum. Mol. Genet. 24, 2349-2359 (2015).
30. Gao, X., Jia, M., Zhang, Y., Breitling, L. P., Brenner, H. DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies. Clin Epigenet 7, 113 (2015).
31. Enroth, S., Enroth, S. B., Johansson, A., Gyllensten, U. Protein profiling reveals consequences of lifestyle choices on predicted biological aging. Sci Rep 5, 17282 (2015).
32. Sen, A. et al. Early life lead exposure causes gender-specific changes in the DNA methylation profile of DNA extracted from dried blood spots. Epigenomics 7, 379-393 (2015).
33. Martin-Ruiz, C. M. et al. Reproducibility of Telomere Length Assessment-An International Collaborative Study. Int J Epidemiol 44, 1749-1754 (2015).
34. Pidsley, R. et al. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 14, 293 (2013).
35. Demerath, E. W. et al. Epigenome-wide association study (EWAS) of BMI, BMI change and waist circumference in African American adults identifies multiple replicated loci. Hum. Mol. Genet. 24, 4464-4479 (2015).
36. Chambers, J. C. et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol. 3, 526-534 (2015).
37. Flanagan, J. M. et al. Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer Epidemiol. Biomarkers Prev. 24, 221-229 (2015).
38. Naeem, H. et al. Reducing the risk of false discovery enabling identification of biologically significant genome-wide methylation status using the HumanMethylation450 array. BMC Genomics 15, 51 (2014).
39. Walker, D. L. et al. DNA methylation profiling: comparison of genome-wide sequencing methods and the Infinium Human Methylation 450 Bead Chip. Epigenomics, 1-16 (2015).
40. Tsai, P. C., Bell, J. T. Power and sample size estimation for epigenome-wide association scans to detect differential DNA methylation Int. J. Epidemiol. 44, 1429-1441 (2015).
41. Rakyan, V. K., Down, T. A., Balding, D. J., Beck, S. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12, 529-541 (2011).
42. Wang, S. Method to detect differentially methylated loci with case-control designs using Illumina arrays. Genet. Epidemiol. 35, 686-694 (2011).
43. Dugue, P. A. et al. The repeatability of DNA methylation measures may also affect the power of epigenome-wide association studies. Int. J. Epidemiol. 44, 1460-1461 (2015).