ArticleFrontiers in genetics2021
Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Multi-Omics Reveals Early Pregnancy Placental Dysfunction Associated With Preterm and Term Preeclampsia.MedComm · 2026Article
- Identifying biomarkers of the serum proteins and metabolites associated with severe preeclampsia.BMC pregnancy and childbirth · 2026Article
- Extracellular Vesicles Alter Trophoblast Function in Pregnancies Complicated by COVID-19.Journal of extracellular vesicles · 2025Article
- Variant graph craft (VGC): a comprehensive tool for analyzing genetic variation and identifying disease-causing variants.BMC bioinformatics · 2024Article
- Pregnancy as a susceptible state for thrombotic microangiopathies.Frontiers in medicine · 2024Review
- Identifying molecular subgroups of patients with preeclampsia through bioinformatics.Frontiers in cardiovascular medicine · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Preeclampsia is a hypertensive disorder of pregnancy, which complicates up to 15% of US deliveries. It is an idiopathic disorder associated with several different phenotypes. We sought to determine if the genetic architecture of preeclampsia can be described by clusters of patients with variants in genes in shared protein interaction networks. We performed a case-control study using whole exome sequencing on early onset preeclamptic mothers with severe clinical features and control mothers with uncomplicated pregnancies between 2016 and 2020. A total of 143 patients were enrolled, 61 women with early onset preeclampsia with severe features based on ACOG criteria, and 82 control women at term, matched for race and ethnicity. A network analysis and visualization tool, Proteinarium, was used to confirm there are clusters of patients with shared gene networks associated with severe preeclampsia. The majority of the sequenced patients appear in two significant clusters. We identified one case dominant and one control dominant cluster. Thirteen genes were unique to the case dominated cluster. Among these genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia. Using bioinformatic analysis, we were able to identify subsets of patients with shared protein interaction networks, thus confirming our hypothesis about the genetic architecture of preeclampsia.
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