Locus-specific Methylation Prediction in Cord Blood and Placenta

By Liming Liang's Lab at Harvard T.H. Chan School of Public Health

Citation:Baoshan Ma, Catherine Allard, Luigi Bouchard, Patrice Perron, Murray Mittleman, Marie-France Hivert, Liming Liang. Locus-specific Methylation Prediction in Cord Blood and Placenta (Epigenetics, 2019).

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. Our previous research suggested that large scale epidemiology studies using easy-to-access surrogate tissues (e.g. blood) could be recalibrated to improve the understanding of epigenetics in hard-to-access tissues (e.g. atrium and artery) and might enable non-invasive disease screening using epigenetic profiles. 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.

This method is easily applicable to other samples and tissues. In order for the investigators to use our method, we have developed an R package which can be used to build the prediction model based on a training dataset with paired surrogate and target tissues, and generate predicted target tissue methylation with only surrogate tissue methylation. The program, detailed instruction and example datasets, as well as supplementary files can be found in the below links. If you find our method and program useful, please cite the above reference.

Download: Package with R function and instructions prediction-software-package-20190218.zip.

Supplementary figures:

Supplementary_Figure_20190218.docx

Supplementary tables:

(1) SupplementaryTable1-R2_comparison_20190218.csv

(2) SupplementaryTable2-clustering_comparison_20190218.docx

(3) SupplementaryTable3-well_predicted_CpGs_20190218.xlsx

(4) SupplementaryTable4-pathway_kegg_20190218.csv