Nature Medicine, Published online: 19 February 2025; doi:10.1038/s41591-025-03536-7
We leveraged early-pregnancy prenatal cell-free DNA screening (PDNAS) sequencing data to determine tissue signatures associated with the development of preeclampsia. Next, we used these epigenetic signatures, as informed by nucleosome positioning, to build machine learning models to classify preeclampsia risk that we validated in distinct internal and external cohorts.
