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dc.contributor.authorGervin, Kristina
dc.contributor.authorSalas, Lucas A
dc.contributor.authorBakulski, Kelly M
dc.contributor.authorvan Zelm, Menno C
dc.contributor.authorKoestler, Devin C
dc.contributor.authorWiencke, John K
dc.contributor.authorDuijts, Liesbeth
dc.contributor.authorMoll, Henriëtte A
dc.contributor.authorKelsey, Karl T
dc.contributor.authorKobor, Michael S
dc.contributor.authorLyle, Robert
dc.contributor.authorChristensen, Brock C
dc.contributor.authorFelix, Janine F
dc.contributor.authorJones, Meaghan J
dc.date.accessioned2019-09-03T05:29:00Z
dc.date.available2019-09-03T05:29:00Z
dc.date.issued2019
dc.identifier.citationClinical Epigenetics. 2019 Aug 27;11(1):125
dc.identifier.urihttp://hdl.handle.net/10852/69848
dc.description.abstractBackground Umbilical cord blood (UCB) is commonly used in epigenome-wide association studies of prenatal exposures. Accounting for cell type composition is critical in such studies as it reduces confounding due to the cell specificity of DNA methylation (DNAm). In the absence of cell sorting information, statistical methods can be applied to deconvolve heterogeneous cell mixtures. Among these methods, reference-based approaches leverage age-appropriate cell-specific DNAm profiles to estimate cellular composition. In UCB, four reference datasets comprising DNAm signatures profiled in purified cell populations have been published using the Illumina 450 K and EPIC arrays. These datasets are biologically and technically different, and currently, there is no consensus on how to best apply them. Here, we systematically evaluate and compare these datasets and provide recommendations for reference-based UCB deconvolution. Results We first evaluated the four reference datasets to ascertain both the purity of the samples and the potential cell cross-contamination. We filtered samples and combined datasets to obtain a joint UCB reference. We selected deconvolution libraries using two different approaches: automatic selection using the top differentially methylated probes from the function pickCompProbes in minfi and a standardized library selected using the IDOL (Identifying Optimal Libraries) iterative algorithm. We compared the performance of each reference separately and in combination, using the two approaches for reference library selection, and validated the results in an independent cohort (Generation R Study, n = 191) with matched Fluorescence-Activated Cell Sorting measured cell counts. Strict filtering and combination of the references significantly improved the accuracy and efficiency of cell type estimates. Ultimately, the IDOL library outperformed the library from the automatic selection method implemented in pickCompProbes. Conclusion These results have important implications for epigenetic studies in UCB as implementing this method will optimally reduce confounding due to cellular heterogeneity. This work provides guidelines for future reference-based UCB deconvolution and establishes a framework for combining reference datasets in other tissues.
dc.language.isoeng
dc.rightsThe Author(s); licensee BioMed Central Ltd.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleSystematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data
dc.typeJournal article
dc.date.updated2019-09-03T05:29:01Z
dc.creator.authorGervin, Kristina
dc.creator.authorSalas, Lucas A
dc.creator.authorBakulski, Kelly M
dc.creator.authorvan Zelm, Menno C
dc.creator.authorKoestler, Devin C
dc.creator.authorWiencke, John K
dc.creator.authorDuijts, Liesbeth
dc.creator.authorMoll, Henriëtte A
dc.creator.authorKelsey, Karl T
dc.creator.authorKobor, Michael S
dc.creator.authorLyle, Robert
dc.creator.authorChristensen, Brock C
dc.creator.authorFelix, Janine F
dc.creator.authorJones, Meaghan J
dc.identifier.doihttps://doi.org/10.1186/s13148-019-0717-y
dc.identifier.urnURN:NBN:no-72918
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/69848/1/13148_2019_Article_717.pdf
dc.type.versionPublishedVersion
cristin.articleid125


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