Aging · 2019 · 170 citations · 67 references
Life SciencesGeneticsMolecular BiologyEpigeneticsGenomicsGestational DiabetesDna Methylation ProfilingGestational Age EstimationPlacental BiologyPlacental PhysiologyReproductive EpigeneticsAge-associated Dna Methylation ChangesAge-related Methylation MarkersEpigenetic Age EstimationEpigenetic ClocksGestational Age MeasurementPlacental EpigeneticsTissue-specific Epigenetic ProfilingTissue-specific Epigenetic States
The human pan-tissue epigenetic clock is widely used for estimating age across the entire lifespan, but it does not lend itself well to estimating gestational age (GA) based on placental DNAm methylation (DNAm) data. We replicate previous findings demonstrating a strong correlation between GA and genome-wide DNAm changes. Using substantially more DNAm arrays (n=1,102 in the training set) than a previous study, we present three new placental epigenetic clocks: 1) a robust placental clock (RPC) which is unaffected by common pregnancy complications (e.g., gestational diabetes, preeclampsia), and 2) a control placental clock (CPC) constructed using placental samples from pregnancies without known placental pathology, and 3) a refined RPC for uncomplicated term pregnancies. These placental clocks are highly accurate estimators of GA based on placental tissue; e.g., predicted GA based on RPC is highly correlated with actual GA (r>0.95 in test data, median error less than one week). We show that epigenetic clocks derived from cord blood or other tissues do not accurately estimate GA in placental samples. While fundamentally different from Horvath's pan-tissue epigenetic clock, placental clocks closely track fetal age during development and may have interesting applications.
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WGCNA: an R package for weighted correlation network analysis
Peter Langfelder, Steve Horvath · BMC Bioinformatics · 2008 · 29.1K citations · Full text
DNA methylation age of human tissues and cell types
Steve Horvath · Genome biology · 2013 · 7.3K citations · Full text