Identifying and correcting epigenetics measurements for systematic sources of variation

TitleIdentifying and correcting epigenetics measurements for systematic sources of variation
Publication TypeJournal Article
Year of Publication2018
AuthorsPerrier F., Novoloaca A., Ambatipudi S., Baglietto L., Ghantous A., Perduca V., Barrdahl M., Harlid S., Ong K.K, Cardona A., Polidoro S., Nost T.H, Overvad K., Omichessan H., Dollé M., Bamia C., Huerta J.M, Vineis P., Herceg Z., Romieu I., Ferrari P.
JournalClin Epigenetics
Volume10
Pagination38
ISBN Number1868-7075
Accession Number29588806
Keywordscentres. This study was also conducted in accordance with the IARC Ethic, claims in published maps and institutional affiliations., Committee (Project No 10-22).Not applicable.The authors declare that they have no, competing interests.Springer Nature remains neutral with regard to jurisdictional, epigenetics, for Research on Cancer, and by the local Ethics Committees in the participating, Methylation, normalization, Pc-pr2, Smoking status
Abstract

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 R(2) 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.

Short TitleClin. EpigeneticsClin. Epigenetics
Alternate JournalClinical epigenetics