PROJECT SUMMARY/ABSTRACT
Preeclampsia occurs in 3% – 6% of women in the US and is a leading source of maternal and fetal morbidity during
pregnancy, immediately after pregnancy and has long term cardiovascular health implications for both mother and child.
Cost of healthcare management of preeclamptic mothers and infants within one year of delivery averages over $2.8 billion
dollars annually. Prevalence of preeclampsia is increasing overtime in the US. African American women have higher
prevalence of preeclampsia, are more likely to have severe preeclampsia and are three times as likely to die as white
Americans due to pregnancy related complications. No effective preventative strategy for preeclampsia exists to-date in part
due to a lack of understanding of causality – be it clinical risk factors, genetic predisposition or socially mediated factors.
Epidemiological studies identify multiple clinical risk factors anad predictors for preeclampsia such as obesity, diabetes,
preexisting hypertension, chronic kidney disease, and thrombophilia. Studies also show that in women without these
preexisting conditions, those with preeclampsia are at higher risk of developing hypertension, chronic kidney disease,
venous thromboembolism, stroke and diabetes 5 to 10 years later. Inferring causality for these associations is difficult with
epidemiologic data alone due to potential for confounding and reverse causation. Preeclampsia, many of its risk factors and
its consequences are heritable with hundreds of genetic variants identified for some traits like blood pressure, diabetes and
kidney function. Since genetic variants do not change during a lifetime and cannot be influenced by reverse causation, and
are less prone to confounding due to Mendel's laws of inheritance which dictate random assortment and segregation of
genes, we can design Mendelian randomization (MR) experiments to use genetic variants as instrumental variables for
exposures to robustly evaluate causality between exposure and outcome under certain assumptions. Coalescing genetic data
on preeclampsia from multiple sources and leveraging existing EHR-linked biobank (BioVU) at Vanderbilt, we form the
PreEclampsia Genetics Network (PEGNet) to study the genetic architecture of preeclampsia in over 28,000 preeclampsia
cases and over 290,000 controls. Using preeclampsia data from PEGNet and recent developments in MR methods, we
propose to evaluate causal relationships between clinical risk factors and predictors of preeclampsia including blood
pressure, kidney function, liver function, obesity and metabolic traits. We will evaluate whether preeclampsia is causally
associated with future cardiovascular complications or if this is due to reverse causality. With emerging MR methods such
as drug target MR, we propose to screen gene and protein targets associated with preeclampsia and also druggable. We
propose MR experiments to validate gene targets for existing drugs in the pipeline for preeclampsia prevention including
aspirin, metformin, statins, and PDE5 blockers. We propose a novel MR framework to elucidate the role colorism, a social
construct of discrimination based on skin color, on preeclampsia risk by using genetic variants of skin pigmentation as
instrumental variables for skin tone. We propose admixture mapping to understand how genetic ancestry influences
preeclampsia in women of African ancestry. Using MR methods in innovative ways, our research informs causal
mechanisms in preeclampsia, the first step necessary to design effective intervention strategies.