Cristin-resultat-ID: 1745501
Sist endret: 20. februar 2020, 14:59
NVI-rapporteringsår: 2019
Resultat
Vitenskapelig artikkel
2019

Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data

Bidragsytere:
  • Kristina Gervin
  • Lucas A. Salas
  • Kelly M. Bakulski
  • Menno C. van Zelm
  • Devin C. Koestler
  • John K Wiencke
  • mfl.

Tidsskrift

Clinical Epigenetics
ISSN 1868-7075
e-ISSN 1868-7083
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2019
Volum: 11
Hefte: 125
Open Access

Importkilder

Scopus-ID: 2-s2.0-85071529691

Beskrivelse Beskrivelse

Tittel

Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data

Sammendrag

Background: 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 referencebased UCB deconvolution and establishes a framework for combining reference datasets in other tissues.

Bidragsytere

Kristina Gervin

  • Tilknyttet:
    Forfatter
    ved Seksjon for galenisk farmasi og samfunns ved Universitetet i Oslo

Lucas A. Salas

  • Tilknyttet:
    Forfatter
    ved Dartmouth College

Kelly M. Bakulski

  • Tilknyttet:
    Forfatter
    ved University of Michigan

Menno C. van Zelm

  • Tilknyttet:
    Forfatter
    ved Monash University
  • Tilknyttet:
    Forfatter
    ved Erasmus MC: Universitair Medisch Centrum Rotterdam

Devin C. Koestler

  • Tilknyttet:
    Forfatter
    ved University of Kansas
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