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Systematic evaluation of DNA methylation age estimation with common preprocessing methods and the Infinium MethylationEPIC BeadChip array

McEwen, Lisa M, Jones, Meaghan J, Lin, David Tse Shen, Edgar, Rachel D, Husquin, Lucas T, MacIsaac, Julia L, Ramadori, Katia E, Morin, Alexander M, Rider, Christopher F, Carlsten, Chris, Quintana-Murci, Lluís, Horvath, Steve, Kobor, Michael S·2018·Clinical Epigenetics

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Crossref · 10.1186/s13148-018-0556-2

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Fig. 1 DNA methylation age comparison between 450K or EPIC Monocyte data across preprocessing methods. Identical samples were assayed on both the 450K and EPIC arrays, and then each preprocessed in one of four ways prior to calculating DNA methylation (DNAm) age: raw unprocessed, GenomeStudio color correction/background subtraction (GS), normal exponential out-of-band (noob) normalization, or quantile normalization. Solid colored line represents corresponding group regression line. For each regression, the Pearson’s correlation coefficient, error (median absolute error between EPIC DNAm age and 450K DNAm age), R2 value, and p value corresponding to the correlation coefficient are shown
Fig. 1 DNA methylation age comparison between 450K or EPIC Monocyte data across preprocessing methods. Identical samples were assayed on both the 450K and EPIC arrays, and then each preprocessed in one of four ways prior to calculating DNA methylation (DNAm) age: raw unprocessed, GenomeStudio color correction/background subtraction (GS), normal exponential out-of-band (noob) normalization, or quantile normalization. Solid colored line represents corresponding group regression line. For each regression, the Pearson’s correlation coefficient, error (median absolute error between EPIC DNAm age and 450K DNAm age), R2 value, and p value corresponding to the correlation coefficient are shownCC BY · PubMed Central
Fig. 2 EPIC DNA methylation age estimated in control samples from the Diesel Exhaust III Study across three tissues. DNA methylation (DNAm) age was estimated using the EPIC 334-CpG model from GenomeStudio background-subtracted and color-channel-adjusted EPIC data. Linear regression line shown with 95% confidence intervals is shown in gray. Error is the median absolute difference between EPIC DNAm age and chronological age. Pearson’s correlation coefficients (r) and corresponding p value are shown for each tissue. BAL = bronchoalveolar lavage, PBMC = peripheral blood mononuclear cells, brush = bronchial brushing
Fig. 2 EPIC DNA methylation age estimated in control samples from the Diesel Exhaust III Study across three tissues. DNA methylation (DNAm) age was estimated using the EPIC 334-CpG model from GenomeStudio background-subtracted and color-channel-adjusted EPIC data. Linear regression line shown with 95% confidence intervals is shown in gray. Error is the median absolute difference between EPIC DNAm age and chronological age. Pearson’s correlation coefficients (r) and corresponding p value are shown for each tissue. BAL = bronchoalveolar lavage, PBMC = peripheral blood mononuclear cells, brush = bronchial brushingCC BY · PubMed Central
Fig. 3 DNA methylation age acceleration variation across preprocessing methods. a Scatter plot of EPIC DNA methylation (DNAm) age calculated from raw and data from three different preprocessing methods: quantile, GenomeStudio (GS), and normal exponential out-of-band (noob) normalization. Colored regression lines and surrounding shaded gray areas represent 95% confidence interval for each group. b Boxplot of estimated DNA methylation (DNAm) age—chronological age (acceleration difference) for each preprocessing method. c Boxplot of residuals from a linear regression (DNAm age~chronological age) across methods. The median is illustrated by horizontal line with upper and lower hinges representing the 25th and 75th percentiles, upper and lower whiskers extend no further than the inter-quartile range multiplied by 1.5. Colored data points represent individual samples for each group
Fig. 3 DNA methylation age acceleration variation across preprocessing methods. a Scatter plot of EPIC DNA methylation (DNAm) age calculated from raw and data from three different preprocessing methods: quantile, GenomeStudio (GS), and normal exponential out-of-band (noob) normalization. Colored regression lines and surrounding shaded gray areas represent 95% confidence interval for each group. b Boxplot of estimated DNA methylation (DNAm) age—chronological age (acceleration difference) for each preprocessing method. c Boxplot of residuals from a linear regression (DNAm age~chronological age) across methods. The median is illustrated by horizontal line with upper and lower hinges representing the 25th and 75th percentiles, upper and lower whiskers extend no further than the inter-quartile range multiplied by 1.5. Colored data points represent individual samples for each groupCC BY · PubMed Central
Fig. 4 Absolute difference between technical replicate pairs for each processing method. The y axis represents the absolute difference between each technical replicate pair’s DNA methylation (DNAm) age. Each processing method is represented on the x axis. The median difference is indicated by the red cross for each group. The color of data points represents one technical replicate pair for ease of interpretation across methods. Error refers to the median absolute difference between DNAm age and chronological age
Fig. 4 Absolute difference between technical replicate pairs for each processing method. The y axis represents the absolute difference between each technical replicate pair’s DNA methylation (DNAm) age. Each processing method is represented on the x axis. The median difference is indicated by the red cross for each group. The color of data points represents one technical replicate pair for ease of interpretation across methods. Error refers to the median absolute difference between DNAm age and chronological ageCC BY · PubMed Central

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Research works printed but not linked: 31

  1. Aryee MJ, et al. Minfi: a flexible and comprehensive bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–9. · DOI
  2. Bibikova M, et al. Genome-wide DNA methylation profiling using Infinium® assay. Epigenomics. 2009;1(1):177–200. · DOI
  3. Breitling LP, et al. Frailty is associated with the epigenetic clock but not with telomere length in a German cohort. Clin Epigenetics. 2016;8(1):21. · DOI
  4. Dedeurwaerder S, et al. A comprehensive overview of Infinium HumanMethylation450 data processing. Brief Bioinform. 2014;15(6):929–41. · DOI
  5. Farré P, et al. Concordant and discordant DNA methylation signatures of aging in human blood and brain. Epigenetics Chromatin. 2015;8(1):19. · DOI
  6. Fortin JP, Triche TJ Jr, Hansen KD. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics. 2016. · DOI
  7. Garagnani P, et al. Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging Cell. 2012;11(6):1132–4. · DOI · Paper page
  8. Hannum G, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359–67. · DOI · Paper page
  9. Hicks SC, Irizarry RA. Quantro: a data-driven approach to guide the choice of an appropriate normalization method. Genome Biol. 2015;16(1):117. · DOI
  10. Horvath S, Levine AJ. HIV-1 infection accelerates age according to the epigenetic clock. J Infect Dis. 2015;212(10):1563–73. · DOI · Paper page
  11. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genetics. 2018;23:223.
  12. Horvath S, Ritz BR. Increased epigenetic age and granulocyte counts in the blood of Parkinson's disease patients. Aging. 2015. · DOI
  13. Horvath S, et al. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 2012;13(10):R97. · DOI
  14. Horvath S, et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 2016;17(1):171. · DOI
  15. Husquin LT, et al. Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation. bioRxiv. 2018:371872. · DOI
  16. Jones PA, Takai D. The role of DNA methylation in mammalian epigenetics. Science (New York, NY). 2001;293(5532):1068–70. · DOI
  17. Logue MW, et al. The correlation of methylation levels measured using Illumina 450K and EPIC BeadChips in blood samples. Epigenomics. 2017;9(11):1363–71. · DOI · Paper page
  18. Marioni RE, Shah S, McRae AF, Chen BH, et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 2015a;16(1):25. · DOI · Paper page
  19. Marioni RE, Shah S, McRae AF, Ritchie SJ, et al. The epigenetic clock is correlated with physical and cognitive fitness in the Lothian birth cohort 1936. Int J Epidemiol. 2015;44(4):1388–96. · DOI
  20. Moran S, Arribas C, Esteller M. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. 2015;8(3):389–99 dx. doi.org . · DOI
  21. Morris TJ, et al. ChAMP: 450k Chip analysis methylation pipeline. Bioinformatics. 2014;30(3):428–30. · DOI
  22. Novick MR, Lewis C. Coefficient alpha and the reliability of composite measurements. Psychometrika. 1967;32(1):1–13. · DOI
  23. Teschendorff AE, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450k DNA methylation data. Bioinformatics. 2013;29(2):189–96. · DOI
  24. Touleimat N, Tost J. Complete pipeline for Infinium® Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics. 2012;4(3):325–41. https://doi.org/10.2217/epi.12.21 . · DOI
  25. Triche TJ, Daniel J. Weisenberger D, Van Den B, Laird PW, Kimberly D. Siegmund; Low-level processing of Illumina Infinium DNA Methylation BeadArrays, Nucleic Acids Research. 201;41(7):e90. https://doi.org/10.1093/nar/gkt090 . · DOI
  26. Triche TJ Jr, et al. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 2013;41(7):e90. · DOI
  27. Troyanskaya O, et al. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001;17(6):520–5. · DOI · Paper page
  28. Wang T, et al. A systematic study of normalization methods for Infinium 450K methylation data using whole-genome bisulfite sequencing data. Epigenetics. 2015;10(7):662–9. · DOI
  29. Weidner CI, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15(2):R24. · DOI · Paper page
  30. Wilhelm-Benartzi CS, et al. Review of processing and analysis methods for DNA methylation array data. Br J Cancer. 2013;109(6):1394–402. · DOI
  31. Yousefi P, et al. Considerations for normalization of DNA methylation data by Illumina 450K BeadChip assay in population studies. Epigenetics. 2014;8(11):1141–52. · DOI

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Metadata

Authors
McEwen, L. M., Jones, M. J., Lin, D. T. S., Edgar, R. D., Husquin, L. T., MacIsaac, J. L., Ramadori, K. E., Morin, A. M., Rider, C. F., Carlsten, C., Quintana-Murci, L., Horvath, S., & Kobor, M. S.
Venue
Clinical Epigenetics
Published
2018-10-16
Volume
10
Issue
1
ISSN
  • 1868-7075
  • 1868-7083
Cited by
147
References
4
Language
en
Funders
  • Canadian Institutes of Health Research (F15-04283)
  • National Institute on Aging (U34AG051425-0)
  • BC Lung Association, MITACS and the Michael Smith Foundation for Health Research
  • Canada Research Chairs (CA) in Occupational and Environmental Lung Disease
  • Canada Research Chairs (CA) in Social Epigenetics
  • Senior Fellow of the Canadian Institute for Advanced Research
  • Sunny Hill BC Leadership Chair in Child Development
  • European Research Council (281297)

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