Computational Genomics of Preeclampsia

In the post genome era, biological research and genomic medicine have been transformed by high-throughput technologies.  New techniques have enabled researchers to investigate biological systems in great detail.  Nonetheless, the extraordinary amount of information in the large number of emerging high-dimension datasets has not been fully exploited.  Increasingly, pathway analysis and other a priori biological knowledge based approaches have improved success in extraction of valuable information from high-throughput experiments and genome-wide association studies.  Preeclampsia is a complex disease and one of the most common causes of fetal and maternal morbidity and mortality worldwide.  It is one of the great but enigmatic health problems.  Despite many studies, there has been little fundamental improvement in our understanding in decades.  It is a multi-system hypertensive disorder of pregnancy, characterized by variable degrees of maternal symptoms including elevated blood pressure, proteinuria and fetal growth retardation that affect 2-8 % of deliveries in the US.  Many clinicians believe there is a difference between preeclampsia and severe or early and late preeclampsia.  However, to date there is little direct evidence that they represent different genetic etiologies.  We hypothesize that preeclampsia is a complex, polygenic disorder that entails activation of a network of genes.  We will perform a case/control study using whole exome sequencing.  We will restrict our enrollment to patients with early, severe preeclampsia.  The working hypothesis is that this will provide better power, lower heterogeneity, and higher genetic effect for this complex phenotype.  We will develop new bioinformatic approaches to identify the gene networks and causal variants that contribute to severe preeclampsia. This will be coupled with high-throughput technologies applied to this carefully chosen cohort of patients.