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

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 contr...

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Bibliographic Details
Published in:BioData mining 2017, Vol.10 (1), p.18-18, Article 18
Main Authors: Tragante, Vinicius, Gho, Johannes M I H, Felix, Janine F, Vasan, Ramachandran S, Smith, Nicholas L, Voight, Benjamin F, Palmer, Colin, van der Harst, Pim, Moore, Jason H, Asselbergs, Folkert W
Format: Article
Language:English
Subjects:
Bioinformatics
Cardiovascular disease
Case studies
Chips (electronics)
Consortia
Coronary artery disease
Coronary vessels
Design analysis
Diabetes
Diseases
Enrichment
Etiology
Gene mapping
Gene set enrichment analyses
Gene set enrichment analysis
Genealogy
Genes
Genetics
Genomes
Genotype & phenotype
Haplotypes
Heart diseases
Heart failure
Loci
Mapping
Methodology
Methods
Ontology
Pipelines
Pruning
Quality control
Single-nucleotide polymorphism
Statistical analysis
Studies
Transcription
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Summary: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. 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. 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.
ISSN:1756-0381
1756-0381
DOI:10.1186/s13040-017-0137-5