Research Paper Volume 11, Issue 12 pp 4238—4253

Placental epigenetic clocks: estimating gestational age using placental DNA methylation levels

Yunsung Lee 1, , Sanaa Choufani 2, , Rosanna Weksberg 3, , Samantha L. Wilson 4, 5, , Victor Yuan 4, 5, , Amber Burt 6, , Carmen Marsit 6, , Ake T. Lu 7, , Beate Ritz 8, , Jon Bohlin 9, , Håkon K. Gjessing 9, 10, , Jennifer R. Harris 1, 9, , Per Magnus 9, , Alexandra M. Binder 8, *, , Wendy P. Robinson 4, 5, *, , Astanand Jugessur 1, 9, 10, *, , Steve Horvath 7, 11, *, ,

  • 1 Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway
  • 2 Genetics and Genome Biology Program, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
  • 3 Genetics and Genome Biology Program, Research Institute, The Hospital for Sick Children and Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
  • 4 Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
  • 5 B.C. Children’s Hospital Research Institute, Vancouver, British Columbia, Canada
  • 6 Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
  • 7 Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
  • 8 Department of Epidemiology, University of California Los Angeles, Los Angeles, CA 90095, USA
  • 9 Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
  • 10 Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
  • 11 Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
* Co-senior authors

received: April 26, 2018 ; accepted: June 17, 2019 ; published: June 24, 2019 ;
How to Cite

Copyright: Lee et al. This is an open‐access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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.


GA: gestational age; DNAm: DNA methylation; LMP: last menstrual period; RPC: robust placental clock; CPC: control placental clock; GEO: Gene Expression Omnibus; 450K: Illumina HumanMethylation 450K BeadChip; EPIC: Illumina MethylationEPIC BeadChip; MAE: median absolute error; funNorm: functional normalization; SWAN: subset-quantiles within arrays; noob: normal-exponential out-of-band; BMIQ: beta-mixture quantile dilation; quanNorm: quantile normalization; dasen: data-driven separate normalization; WGCNA: weighted gene co-expression network analysis; EWAS: epigenome-wide association study; cffDNA: cell-free fetal DNA.