Gene Set Enrichment Analyses: Lessons learned from the heart failure phenotype

Vinicius Tragante, Johannes M.I.H. Gho, Janine F. Felix, Ramachandran S. Vasan, Nicholas L. Smith, Benjamin F. Voight, Colin Palmer, Pim Van Der Harst, Jason H. Moore, Folkert W. Asselbergs

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Genetic 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. Results: Approaches 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. Conclusions: This 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.

Original languageEnglish (US)
Article number18
JournalBioData Mining
Volume10
Issue number1
DOIs
StatePublished - 2017
Externally publishedYes

Keywords

  • Coronary artery disease
  • Gene set enrichment analyses
  • Heart failure

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computer Science Applications
  • Computational Mathematics
  • Computational Theory and Mathematics

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