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Background: Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed., Methods: We identified a cohort of N=1125 pregnant individuals who delivered between May 2015 and May 2022 at Mass General Brigham Hospitals with available electronic health record data and linked genetic data. Using clinical electronic health record data and systolic blood pressure polygenic risk scores derived from a large genome-wide association study, we developed machine learning (XGBoost) and logistic regression models to predict preeclampsia risk., Results: Pregnant individuals with a systolic blood pressure polygenic risk score in the top quartile had higher blood pressures throughout pregnancy compared with patients within the lowest quartile systolic blood pressure polygenic risk score. In the first trimester, the most predictive model was XGBoost, with an area under the curve of 0.74. In late pregnancy, with data obtained up to the delivery admission, the best-performing model was XGBoost using clinical variables, which achieved an area under the curve of 0.91. Adding the systolic blood pressure polygenic risk score to the models did not improve the performance significantly based on De Long test comparing the area under the curve of models with and without the polygenic score., Conclusions: Integrating clinical factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes., Competing Interests: Disclosures V.P. Kovacheva and R.Y. Cohen report consulting fees from Avania CRO and patent WO2021119593A1 for control of a therapeutic delivery system assigned to the Mass General Brigham. K. Gray has served as a consultant to Illumina, Inc, Aetion, Roche, and BillionToOne. The other authors report no conflicts.

The genetic susceptibility to preeclampsia, a pregnancy-specific complication with significant maternal and fetal morbidity, has been poorly characterized. To identify maternal genes associated with preeclampsia risk, we assembled 498 cases and 1864 controls of European ancestry from preeclampsia case-control collections in 5 different US sites (with additional matched population controls), genotyped samples on a cardiovascular gene-centric array composed of variants from ≈2000 genes selected based on prior genetic studies of cardiovascular and metabolic diseases and performed case-control genetic association analysis on 27 429 variants passing quality control. In silico replication testing of 9 lead signals with P <10 - 4 was performed in independent European samples from the SOPHIA (Study of Pregnancy Hypertension in Iowa) and Inova cohorts (212 cases, 456 controls). Multiethnic assessment of lead signals was then performed in samples of black (26 cases, 136 controls), Hispanic (132 cases, 468 controls), and East Asian (9 cases, 80 controls) ancestry. Multiethnic meta-analysis (877 cases, 3004 controls) revealed a study-wide statistically significant association of the rs9478812 variant in the pleiotropic PLEKHG1 gene (odds ratio, 1.40 [1.23-1.60]; P meta =5.90×10 -7 ). The rs9478812 effect was even stronger in the subset of European cases with known early-onset preeclampsia (236 cases diagnosed <37 weeks, 1864 controls; odds ratio, 1.59 [1.27-1.98]; P =4.01×10 -5 ). PLEKHG1 variants have previously been implicated in genome-wide association studies of blood pressure, body weight, and neurological disorders. Although larger studies are required to further define maternal preeclampsia heritability, this study identifies a novel maternal risk locus for further investigation., (© 2018 American Heart Association, Inc.)