Understanding causal mechanisms in preeclampsia through genetic instrumental variables

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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.