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dc.contributor.authorPerrier, Flavie
dc.contributor.authorNovoloaca, Alexei
dc.contributor.authorAmbatipudi, Srikant
dc.contributor.authorBaglietto, Laura
dc.contributor.authorGhantous, Akram
dc.contributor.authorPerduca, Vittorio
dc.contributor.authorBarrdahl, Myrto
dc.contributor.authorHarlid, Sophia
dc.contributor.authorOng, Ken K
dc.contributor.authorCardona, Alexia
dc.contributor.authorPolidoro, Silvia
dc.contributor.authorNøst, Therese Haugdahl
dc.contributor.authorOvervad, Kim
dc.contributor.authorOmichessan, Hanane
dc.contributor.authorDollé, Martijn
dc.contributor.authorBamia, Christina
dc.contributor.authorHuerta, José María
dc.contributor.authorVineis, Paolo
dc.contributor.authorHerceg, Zdenko
dc.contributor.authorRomieu, Isabelle
dc.contributor.authorFerrari, Pietro
dc.date.accessioned2019-03-06T14:38:02Z
dc.date.available2019-03-06T14:38:02Z
dc.date.issued2018-03-21
dc.description.abstract<i>Background</i>: 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.<p> <p><i>Results</i>: A sizeable proportion of systematic variability due to variables expressing ‘batch’ and ‘sample position’ within ‘chip’ was identified, with values of the partial R<sup>2</sup> 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 (<i>k</i> = 600 and <i>k</i> = 427, respectively) were associated to smoking status than the SVA correction (<i>k</i> = 96).<p> <p><i>Conclusions</i>: 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.<p>en_US
dc.description.sponsorship‘Fondation de France’ Institut National du Cancer International Agency for Research on Cancer Swedish Cancer Society Swedish Research Council County Councils of Skåne and Västerbotten MRC programme UiT - the Arctic University of Norway The Hellenic Health Foundationen_US
dc.descriptionSource at <a href=https://doi.org/10.1186/s13148-018-0471-6>https://doi.org/10.1186/s13148-018-0471-6. </a> © The Author(s). 2018en_US
dc.identifier.citationPerrier, F., Novoloaca, A., Ambatipudi, S., Baglietto, L., Ghantous, A., Perduca, V. ... Ferrari, P. (2018). Identifying and correcting epigenetics measurements for systematic sources of variation. <i>Clinical Epigenetics</i>, 10:38. https://doi.org/10.1186/s13148-018-0471-6en_US
dc.identifier.cristinIDFRIDAID 1626347
dc.identifier.doi10.1186/s13148-018-0471-6
dc.identifier.issn1868-7075
dc.identifier.issn1868-7083
dc.identifier.urihttps://hdl.handle.net/10037/14874
dc.language.isoengen_US
dc.publisherClinical Epigenetics Societyen_US
dc.relation.journalClinical Epigenetics
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7-HEALTH/260791/EU/Specific Programme "Cooperation": Health/en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/?/EU/Marie Curie Actions - People - Cofunding of regional, national and international programmes/COFUND/en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectEpigeneticsen_US
dc.subjectPC-PR2en_US
dc.subjectNormalizationen_US
dc.subjectMethylationen_US
dc.subjectSmoking statusen_US
dc.subjectVDP::Medical disciplines: 700::Health sciences: 800::Community medicine, Social medicine: 801en_US
dc.subjectVDP::Medisinske Fag: 700::Helsefag: 800::Samfunnsmedisin, sosialmedisin: 801en_US
dc.titleIdentifying and correcting epigenetics measurements for systematic sources of variationen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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