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

TY - JOUR

T1 - Gene Set Enrichment Analyses

T2 - lessons learned from the heart failure phenotype

AU - Tragante, Vinicius

AU - Gho, Johannes M. I. H.

AU - Felix, Janine F.

AU - Vasan, Ramachandran S.

AU - Smith, Nicholas L.

AU - Voight, Benjamin F.

AU - Palmer, Colin

AU - van der Harst, Pim

AU - Moore, Jason H.

AU - Asselbergs, Folkert W.

AU - CHARGE Heart Failure Working Group

N1 - This work was supported by NIH R01 LM010098. Folkert W. Asselbergs is supported by a Dekker scholarship-Junior Staff Member 2014T001 – Netherlands Heart Foundation, UCL Hospitals NIHR Biomedical Research Centre, and by a clinical fellowship from the Netherlands Organization for Health Research (ZonMw grant 90700342). The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/20072013 under grant agreement n° HEALTHF22013601456 (CVgenesattarget).

PY - 2017/5/26

Y1 - 2017/5/26

N2 - Background: Genetic studies for complex diseases have predominantly discoveredmain effects at individual loci, but have not focused on genomic and environmentalcontexts important for a phenotype. Gene Set Enrichment Analysis (GSEA) aims toaddress this by identifying sets of genes or biological pathways contributing to aphenotype, through gene-gene interactions or other mechanisms, which are not thefocus of conventional association methods. Results: Approaches that utilize GSEA can now take input from array chips, eithergene-centric or genome-wide, but are highly sensitive to study design, SNP selectionand pruning strategies, SNP-to-gene mapping, and pathway definitions. Here, wepresent lessons learned from our experience with GSEA of heart failure, a particularlychallenging phenotype due to its underlying heterogeneous etiology. Conclusions: This case study shows that proper data handling is essential to avoidfalse-positive results. Well-defined pipelines for quality control are needed to avoidreporting spurious results using GSEA.

AB - Background: Genetic studies for complex diseases have predominantly discoveredmain effects at individual loci, but have not focused on genomic and environmentalcontexts important for a phenotype. Gene Set Enrichment Analysis (GSEA) aims toaddress this by identifying sets of genes or biological pathways contributing to aphenotype, through gene-gene interactions or other mechanisms, which are not thefocus of conventional association methods. Results: Approaches that utilize GSEA can now take input from array chips, eithergene-centric or genome-wide, but are highly sensitive to study design, SNP selectionand pruning strategies, SNP-to-gene mapping, and pathway definitions. Here, wepresent lessons learned from our experience with GSEA of heart failure, a particularlychallenging phenotype due to its underlying heterogeneous etiology. Conclusions: This case study shows that proper data handling is essential to avoidfalse-positive results. Well-defined pipelines for quality control are needed to avoidreporting spurious results using GSEA.

KW - Gene set enrichment analyses

KW - Heart failure

KW - Coronary artery disease

U2 - 10.1186/s13040-017-0137-5

DO - 10.1186/s13040-017-0137-5

M3 - Article

C2 - 28559929

SN - 1756-0381

VL - 10

SP - 1

EP - 11

JO - BioData Mining

JF - BioData Mining

M1 - 18

ER -