Integrating multi-omics data to understand the heterogeneity of preeclampsia and long-termpostpartum hypertension - PROJECT SUMMARY Preeclampsia (PE) is a pregnancy-specific syndrome of progressive vascular dysfunction that leads to escalating hypertension and extensive maternal organ damage, which can ultimately result in death. Occurring in 5-8% of pregnancies worldwide, PE is a leading cause of both maternal and perinatal mortality. Beyond the short-term risks, PE survivors suffer a 2-6-fold increase in lifelong risk of future hypertension (HTN), heart failure, stroke, and cardiovascular (CV)-specific mortality. Despite its high incidence, the underlying genetic and environmental contributions to PE is lacking. Recent advances now allow for the simultaneous analysis of multiple types of omics data (“multi-omics”), offering new opportunities to identify risk factors for PE and its long-term CV effects. In this project, we will use TOPMed- supported omics data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be Heart Health Study (nuMoM2b-HHS) as well as supplementary data from its parent study, nuMoM2b. Both cohorts possess extensive phenotyping variables. These variables were collected in a standardized manner throughout the course of pregnancy and the postpartum period, providing an unparalleled level of depth in a large cohort with long-term follow-up. The nuMoM2b and nuMoM2b-HHS cohorts, with their diverse sample types, large cohort size, comprehensive clinical and psychosocial phenotyping, and long-term CV follow-up, provide an unparalleled platform for a holistic analysis of the factors contributing to the increasing burden of CV morbidity associated with PE. The long-term goal of our study is to identify risk variants, gene targets and pathways for PE and its associated long-term CV effects. Our current objective is to perform a first-time analysis of whole genome sequencing (WGS) data in the nuMoM2b-HHS cohort together with an integrated analysis of multidimensional data from the nuMoM2b-HHS cohort. To achieve our objective, we propose the following aims. Aim 1: Determine the genetic architecture of PE (and its long-term CV effects) and gene environment interactions. Aim 2: Determine the role of placental trophoblast gene expression in the development of PE and long-term CV effects and perform in- depth multi-omics data integration to provide molecular insights into PE. Aim 3: Optimize predictive models for PE (and long-term CV effects) through machine learning and integration of clinical, environmental, and multi- omics data. Completion of these aims will provide one of the most comprehensive models of PE to date, that integrates data from both the maternal cardiovascular system and placenta-fetal interface. This work will lay the groundwork for further investigation of the reproductive origins of PE, CV morbidity and health across the lifespan.
