PROJECT SUMMARY Preeclampsia (PE), a pregnancy-specific condition characterized by hypertension and other organ system dysfunction, is a major contributor to worldwide morbidity and mortality for both the mother and offspring. During PE, pregnant individuals may experience life-threatening complications and they harbor lifelong risks for cardiovascular disease, while the offspring may experience complications related to prematurity. High-quality evidence supports the use of low-dose aspirin to reduce the risk of PE in those at high-risk, and emerging data suggests that tighter blood pressure control may also mitigate the risks. Identification of pregnancies at high-risk, however, currently relies on crude assessment of clinical risk factors alone with poor positive predictive values. Although screening programs outside of the United States (US) report high detection rates, many include maternal clinical factors, serum biomarkers, and ultrasound measures. Testing of performance in a US population has not been possible as these included metrics are not routinely measured as part of standard prenatal care. Although PE is rooted in placental dysfunction, with aberrations, such as epigenetic modifications, occurring early in pregnancy, the placenta remains inaccessible during pregnancy. Recent data suggests that placental derivatives (RNA, cell-free DNA) circulating in maternal circulation can be interrogated to understand developing pregnancy complications, including PE. Our key insight is that the sequencing data generated via cell-free DNA based non-invasive prenatal testing (NIPT) for aneuploidy screening can be leveraged to infer epigenetic signatures specific to the placenta. With testing of over 1000 samples, we have found that epigenetic signatures (in this case, nucleosome positioning) can be reliably inferred based on available sequencing data, and that machine learning modeling can be used to predict early-onset PE. In the proposed studies, Kanona, Inc and collaborators will build upon this work to refine/correct technical and analytic variables, perform validation on a separate and distinct cohort, and optimize the methodology for PE risk classification from cfDNA through sequencing of placental samples and deeper sequencing of first trimester cfDNA samples. With the studies proposed here, and follow-up phase II external validation studies, Kanona, Inc. will develop a test for PE prediction that can be easily performed in conjunction with cell-free DNA based NIPT without the need for additional blood samples or dedicated laboratory workflows. This has the potential to significantly alter the landscape of prenatal care, providing an element of precision medicine with potentially actionable information that can prompt initiation of interventions or tailored monitoring programs to ultimately decrease the incidence and potentially severity of PE.