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

Area of Science:

• Genetics
• Bioinformatics
• Genomic Medicine

Background:

• Complex disease genetic studies often miss the broader genomic and environmental contexts of phenotypes.
• Gene Set Enrichment Analysis (GSEA) offers a method to identify biological pathways and gene sets involved in disease phenotypes, going beyond individual genetic effects.
• Conventional association methods do not typically focus on gene-gene interactions or pathway-level contributions.

Purpose of the Study:

• To present lessons learned from applying Gene Set Enrichment Analysis (GSEA) to the complex phenotype of heart failure.
• To highlight the challenges and critical considerations in utilizing GSEA for complex disease research.

Main Methods:

• Utilized array chip data (gene-centric or genome-wide) as input for GSEA.
• Focused on the practical aspects and potential pitfalls in GSEA, including study design, SNP selection, SNP-to-gene mapping, and pathway definitions.
• Applied GSEA methodology to a case study of heart failure, a phenotype with a heterogeneous etiology.

Main Results:

• GSEA approaches are sensitive to various methodological choices, including study design, data preprocessing, and pathway definitions.
• The application of GSEA to heart failure revealed the importance of careful consideration of these factors due to the phenotype's complexity.
• Experience highlighted the impact of methodological choices on the reliability of GSEA findings.

Conclusions:

• Robust data handling is essential for accurate results in GSEA.
• Implementing well-defined quality control pipelines is necessary to prevent the reporting of spurious findings in GSEA.
• The study underscores the need for rigorous methodological standards in applying GSEA to complex diseases.