ArticleFrontiers in cell and developmental biology2020
Integrate Molecular Phenome and Polygenic Interaction to Detect the Genetic Risk of Ischemic Stroke.
Article in Frontiers in cell and developmental biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Genetic insights into 5-LOX-activating protein: a narrative review of disease associations.Human genomics · 2025Review
- Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Functional polymorphism in miR-208 is associated with increased risk for ischemic stroke.BMC medical genomics · 2023Article
- Targeting leukotriene biosynthesis to prevent atherosclerotic cardiovascular disease.Conditioning medicine · 2023Article
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ischemic stroke (IS) is one of the leading causes of death, and the genetic risk of which are continuously calculated and detected by association study of single nucleotide polymorphism (SNP) and the phenotype relations. However, the systematic assessment of IS risk still needs the accumulation of molecular phenotype and function from the level of omics. In this study, we integrated IS phenome, polygenic interaction gene expression and molecular function to screen the risk gene and molecular function. Then, we performed a case-control study including 507 cases and 503 controls to verify the genetic associated relationship among the candidate functional genes and the IS phenotype in a northern Chinese Han population. Mediation analysis revealed that the blood pressure, high density lipoprotein (HDL) and glucose mediated the potential effect of SOCS1, CD137, ALOX5AP, RNLS, and KALRN in IS, both for the functional analysis and genetic association. And the SNP-SNP interactions analysis by multifactor dimensionality reduction (MDR) approach also presented a combination effect of IS risk. The further interaction network and gene ontology (GO) enrichment analysis suggested that CD137 and KALRN functioning in inflammatory could play an expanded role during the pathogenesis and progression of IS. The present study opens a new avenue to evaluate the underlying mechanisms and biomarkers of IS through integrating multiple omics information.
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