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.

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BioData Mining
Erasmus MC: University Medical Center Rotterdam

Tragante, V., Gho, J., Felix, J., Vasan, R.S. (Ramachandran S.), Smith, N., Voight, B., … Asselbergs, F. (2017). Gene Set Enrichment Analyses: Lessons learned from the heart failure phenotype. BioData Mining, 10(1). doi:10.1186/s13040-017-0137-5