Abstract
Placenta-origin pregnancy complications, including preeclampsia (PE), gestational diabetes mellitus (GDM), fetal growth restriction (FGR), and macrosomia (MA) are common occurrences in pregnancy, resulting in significant morbidity and mortality for both mother and fetus. However, despite their frequency, there are no reliable methods for the early diagnosis of these complications. Since cfDNA is mainly derived from placental trophoblasts and maternal hematopoietic cells, it might have information for gene expression which can be used for disease prediction. Here, low coverage whole-genome sequencing on plasma DNA from 2,199 pregnancies is performed based on retrospective cohorts of 3,200 pregnant women. Read depth in the promoter regions is examined to define read-depth distribution patterns of promoters for pregnancy complications and controls. Using machine learning methods, classifiers for predicting pregnancy complications are developed. Using these classifiers, complications are successfully predicted with an accuracy of 80.3%, 78.9%, 72.1%, and 83.0% for MA, FGR, GDM, and PE, respectively. The findings suggest that promoter profiling of cfDNA may be used as a biological biomarker for predicting pregnancy complications at early gestational age.