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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 Marìa
dc.contributor.authorVineis, Paolo
dc.contributor.authorHerceg, Zdenko
dc.contributor.authorRomieu, Isabelle
dc.contributor.authorFerrari, Pietro
dc.date.accessioned2018-04-19T09:52:20Z
dc.date.available2018-04-19T09:52:20Z
dc.date.issued2018
dc.identifier.citationIdentifying and correcting epigenetics measurements for systematic sources of variation. 2018, 10:38 Clin Epigeneticsen
dc.identifier.issn1868-7083
dc.identifier.pmid29588806
dc.identifier.doi10.1186/s13148-018-0471-6
dc.identifier.urihttp://hdl.handle.net/10029/621807
dc.description.abstractMethylation 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.
dc.language.isoenen
dc.rightsArchived with thanks to Clinical epigeneticsen
dc.titleIdentifying and correcting epigenetics measurements for systematic sources of variation.en
dc.typeArticleen
dc.identifier.journalClin Epigenetics 2018; 10:38en
html.description.abstractMethylation 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.


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