Dataset Information

Effect of prematurity on genome wide methylation in the placenta.

ABSTRACT: BACKGROUND:Preterm birth is a significant clinical problem and an enormous burden on society, affecting one in eight pregnant women and their newborns. Despite decades of research, the molecular mechanism underlying its pathogenesis remains unclear. Many studies have shown that preterm birth is associated with health risks across the later life course. The "fetal origins" hypothesis postulates that adverse intrauterine exposures are associated with later disease susceptibility. Our recent studies have focused on the placental epigenome at term. We extended these studies to genome-wide placental DNA methylation across a wide range of gestational ages. We applied methylation dependent immunoprecipitation/DNA sequencing (MeDIP-seq) to 9 placentas with gestational age from 25?weeks to term to identify differentially methylated regions (DMRs). RESULTS:Enrichment analysis revealed 427 DMRs with nominally significant differences in methylation between preterm and term placentas (p?

SUBMITTER: Schuster J

PROVIDER: S-EPMC6599230 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effect of prematurity on genome wide methylation in the placenta.

Schuster Jessica J Uzun Alper A Stablia Joan J Schorl Christoph C Mori Mari M Padbury James F JF

BMC medical genetics 20190628 1

<h4>Background</h4>Preterm birth is a significant clinical problem and an enormous burden on society, affecting one in eight pregnant women and their newborns. Despite decades of research, the molecular mechanism underlying its pathogenesis remains unclear. Many studies have shown that preterm birth is associated with health risks across the later life course. The "fetal origins" hypothesis postulates that adverse intrauterine exposures are associated with later disease susceptibility. Our recen ...[more]

PMID: 31253109

Similar Datasets

Project description:BACKGROUND: Genome-wide methylation profiling has led to more comprehensive insights into gene regulation mechanisms and potential therapeutic targets. Illumina Human Methylation BeadChip is one of the most commonly used genome-wide methylation platforms. Similar to other microarray experiments, methylation data is susceptible to various technical artifacts, particularly batch effects. To date, little attention has been given to issues related to normalization and batch effect correction for this kind of data. METHODS: We evaluated three common normalization approaches and investigated their performance in batch effect removal using three datasets with different degrees of batch effects generated from HumanMethylation27 platform: quantile normalization at average ? value (QN?); two step quantile normalization at probe signals implemented in "lumi" package of R (lumi); and quantile normalization of A and B signal separately (ABnorm). Subsequent Empirical Bayes (EB) batch adjustment was also evaluated. RESULTS: Each normalization could remove a portion of batch effects and their effectiveness differed depending on the severity of batch effects in a dataset. For the dataset with minor batch effects (Dataset 1), normalization alone appeared adequate and "lumi" showed the best performance. However, all methods left substantial batch effects intact in the datasets with obvious batch effects and further correction was necessary. Without any correction, 50 and 66 percent of CpGs were associated with batch effects in Dataset 2 and 3, respectively. After QN?, lumi or ABnorm, the number of CpGs associated with batch effects were reduced to 24, 32, and 26 percent for Dataset 2; and 37, 46, and 35 percent for Dataset 3, respectively. Additional EB correction effectively removed such remaining non-biological effects. More importantly, the two-step procedure almost tripled the numbers of CpGs associated with the outcome of interest for the two datasets. CONCLUSION: Genome-wide methylation data from Infinium Methylation BeadChip can be susceptible to batch effects with profound impacts on downstream analyses and conclusions. Normalization can reduce part but not all batch effects. EB correction along with normalization is recommended for effective batch effect removal.

Dataset Information

Effect of prematurity on genome wide methylation in the placenta.

Publications

Effect of prematurity on genome wide methylation in the placenta.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure