Article open access publication

Identifying and correcting epigenetics measurements for systematic sources of variation

Clinical Epigenetics, Springer Nature, ISSN 1868-7075

Volume 10, 1, 2018

DOI:10.1186/s13148-018-0471-6, Dimensions: pub.1101671554, PMC: PMC5863487, PMID: 29588806,



  1. (1) International Agency For Research On Cancer, grid.17703.32
  2. (2) University of Pisa, grid.5395.a
  3. (3) Paris Descartes University, grid.10992.33
  4. (4) German Cancer Research Center, grid.7497.d
  5. (5) Umeå University, grid.12650.30
  6. (6) Institute of Metabolic Science, grid.470900.a
  7. (7) IIGM – Italian Institute for Genomic Medicine, Torino, Italy
  8. (8) The Arctic University of Norway, grid.10919.30
  9. (9) Aalborg Hospital, grid.27530.33, North Denmark Region
  10. (10) Aarhus University, grid.7048.b, AU
  11. (11) Centre for research in epidemiology and population health, grid.463845.8
  12. (12) Institut Gustave Roussy, grid.14925.3b
  13. (13) National Institute for Public Health and the Environment, grid.31147.30
  14. (14) Hellenic Health Foundation, grid.424637.0
  15. (15) National and Kapodistrian University of Athens, grid.5216.0
  16. (16) Institute of Health Carlos III, grid.413448.e
  17. (17) Instituto Murciano de Investigación Biosanitaria, grid.452553.0
  18. (18) Imperial College London, grid.7445.2


Background: Methylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features.In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis. Results: A sizeable proportion of systematic variability due to variables expressing 'batch' and 'sample position' within 'chip' was identified, with values of the partial R2 statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals' methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to 'batch' (1.3%) and 'sample position' (0.6%), and in a diminished variability attributable to 'chip' within a batch (0.9%). After ComBat or the residuals' corrections, a larger number of significant sites (k = 600 and k = 427, respectively) were associated to smoking status than the SVA correction (k = 96). Conclusions: The three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation.


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