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Maternal cardiovascular-related single nucleotide polymorphisms, genes, and pathways associated with early-onset preeclampsia.

Paula Benny | Kelly Yamasato | Breck Yunits | Xun Zhu | Travers Ching | Lana X Garmire | Marla J Berry | Dena Towner
PloS one | 2019

Preeclampsia is a medical condition complicated with hypertension and proteinuria during pregnancy. While preeclampsia affects approximately 5% of pregnancies, it remains without a cure. In addition, women who had preeclampsia during pregnancy have been reported to have an increased risk for cardiovascular disease later in life. However, the disease etiology and molecular mechanisms remain poorly understood. The paucity in the literature on preeclampsia associated maternal cardiovascular risk in different ethnic populations also present a need for more research. Therefore, the objective of this study was to identify cardiovascular/metabolic single nucleotide polymorphisms (SNPs), genes, and regulatory pathways associated with early-onset preeclampsia.

Pubmed ID: 31557190

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Associated grants

  • Agency: NLM NIH HHS, United States
    Id: R01 LM012907
  • Agency: NLM NIH HHS, United States
    Id: R01 LM012373
  • Agency: NIMHD NIH HHS, United States
    Id: U54 MD007601
  • Agency: NIMHD NIH HHS, United States
    Id: U54 MD007584
  • Agency: NICHD NIH HHS, United States
    Id: R01 HD084633
  • Agency: NIGMS NIH HHS, United States
    Id: P20 GM103457
  • Agency: NIEHS NIH HHS, United States
    Id: K01 ES025434
  • Agency: NIMHD NIH HHS, United States
    Id: P20 MD000173
  • Agency: NIMHD NIH HHS, United States
    Id: G12 MD007601
  • Agency: NIDDK NIH HHS, United States
    Id: R01 DK047320

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Ingenuity Pathway Analysis (tool)

RRID:SCR_008653

A web-based software application that enables users to analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays, metabolomics, proteomics, and RNA-Seq experiments, and small-scale experiments that generate gene and chemical lists. Users can search for targeted information on genes, proteins, chemicals, and drugs, and build interactive models of experimental systems. IPA allows exploration of molecular, chemical, gene, protein and miRNA interactions, creation of custom molecular pathways, and the ability to view and modify metabolic, signaling, and toxicological canonical pathways. In addition to the networks and pathways that can be created, IPA can provide multiple layering of additional information, such as drugs, disease genes, expression data, cellular functions and processes, or a researchers own genes or chemicals of interest.

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