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National Institute of Environmental Health Sciences

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Publication Detail

Title: Locus-specific DNA methylation prediction in cord blood and placenta.

Authors: Ma, Baoshan; Allard, Catherine; Bouchard, Luigi; Perron, Patrice; Mittleman, Murray A; Hivert, Marie-France; Liang, Liming

Published In Epigenetics, (2019 04)

Abstract: DNA methylation is known to be responsive to prenatal exposures, which may be a part of the mechanism linking early developmental exposures to future chronic diseases. Many studies use blood to measure DNA methylation, yet we know that DNA methylation is tissue specific. Placenta is central to fetal growth and development, but it is rarely feasible to collect this tissue in large epidemiological studies; on the other hand, cord blood samples are more accessible. In this study, based on paired samples of both placenta and cord blood tissues from 169 individuals, we investigated the methylation concordance between placenta and cord blood. We then employed a machine-learning-based model to predict locus-specific DNA methylation levels in placenta using DNA methylation levels in cord blood. We found that methylation correlation between placenta and cord blood is lower than other tissue pairs, consistent with existing observations that placenta methylation has a distinct pattern. Nonetheless, there are still a number of CpG sites showing robust association between the two tissues. We built prediction models for placenta methylation based on cord blood data and documented a subset of 1,012 CpG sites with high correlation between measured and predicted placenta methylation levels. The resulting list of CpG sites and prediction models could help to reveal the loci where internal or external influences may affect DNA methylation in both placenta and cord blood, and provide a reference data to predict the effects on placenta in future study even when the tissue is not available in an epidemiological study.

PubMed ID: 30885044 Exiting the NIEHS site

MeSH Terms: CpG Islands; DNA Methylation*; Female; Fetal Blood/metabolism*; Genetic Loci*; Humans; Machine Learning; Models, Genetic*; Organ Specificity; Placenta/metabolism*; Pregnancy

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