henhaobio
Sign in

Biology of Sex DifferencesGenomicsPeer-reviewed journal article

A cross-cohort analysis of autosomal DNA methylation sex differences in the term placenta

Amy M. InksteriD, Victor YuaniD, Chaini KonwariD, Allison MatthewsiD, Carolyn J. BrowniD, Wendy P. RobinsoniD

PDFDOIPreprint

Abstract

BackgroundHuman placental DNA methylation (DNAme) data is a valuable resource for studying sex differences during gestation, as DNAme profiles after delivery reflect the cumulative effects of gene expression patterns and exposures across gestation. Here, we present an analysis of sex differences in autosomal patterns of DNAme in the uncomplicated term placenta (n=343) using the Illumina 450K array. ResultsUsing a false discovery rate 0.10, we identified 162 autosomal CpG sites that were differentially methylated by sex, and that replicated in an independent cohort of samples (n=293). Several of these differentially methylated CpG sites were part of larger correlated regions of differential DNAme, and many also exhibited sex-specific DNAme variability. Although global DNAme levels did not differ by sex, the majority of significantly differentially methylated CpGs were more highly methylated in male placentae, the opposite of what is seen in differential methylation analyses of somatic tissues. Interestingly, patterns of autosomal DNAme at these significantly differentially methylated CpGs organized placental samples along a continuum, rather than into discrete male and female clusters, and sample position along the continuum was significantly associated with maternal age and newborn birthweight standard deviation. ConclusionsOur results provide a comprehensive analysis of sex differences in autosomal DNAme in the term human placenta. We report a list of high-confidence autosomal sex-associated differentially methylated CpGs, and identify several key features of these loci that suggest their relevance to sex differences observed in normative and complicated pregnancies.

Citations
46
FWCI
4.16
Field percentile
top 4%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISAndrews '22Legault '24Grant '22Ladd‐Acosta '21Lemaire '25Yanagida '25
85% semantic matchGenomics
Shan V. Andrews, Irene J. Yang, Karolin Fröhlich+2 more·Scientific Reports·Jun 7, 2022· 32 citations