ArticleBioData mining2017
Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype.
Article in BioData mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A practical guide to functional enrichment analysis.Frontiers in bioinformatics · 2026Article
- Unlocking the genetic code: a comprehensive Genome-Wide association study and gene set enrichment analysis of cell-mediated immunity in chickens.BMC genomics · 2025Article
- Integration of bioinformatics analysis, molecular docking and animal experiments to study the therapeutic mechanisms of berberine against allergic rhinitis.Scientific reports · 2024Article
- Gene-Interaction-Sensitive enrichment analysis in congenital heart disease.BioData mining · 2022Article
- Inflammation-related genes and immune infiltration landscape identified in kainite-induced temporal lobe epilepsy based on integrated bioinformatics analysis.Frontiers in neuroscience · 2022Article
- The Identification of Key Genes and Biological Pathways in Heart Failure by Integrated Bioinformatics Analysis.Computational and mathematical methods in medicine · 2021Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundGenetic studies for complex diseases have predominantly discovered main effects at individual loci, but have not focused on genomic and environmental contexts important for a phenotype. Gene Set Enrichment Analysis (GSEA) aims to address this by identifying sets of genes or biological pathways contributing to a phenotype, through gene-gene interactions or other mechanisms, which are not the focus of conventional association methods.
resultsApproaches that utilize GSEA can now take input from array chips, either gene-centric or genome-wide, but are highly sensitive to study design, SNP selection and pruning strategies, SNP-to-gene mapping, and pathway definitions. Here, we present lessons learned from our experience with GSEA of heart failure, a particularly challenging phenotype due to its underlying heterogeneous etiology.
conclusionsThis case study shows that proper data handling is essential to avoid false-positive results. Well-defined pipelines for quality control are needed to avoid reporting spurious results using GSEA.
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Registered trials
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