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

Affiliations

¹ Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
² Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
³ Departments of Medicine and Preventive Medicine, Boston University School of Medicine, Boston, MA USA.
⁴ Department of Epidemiology, University of Washington, Seattle, WA USA.
⁵ Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
⁶ Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
⁷ Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
⁸ Population Pharmacogenetics Group, University of Dundee, Dundee, UK.
⁹ Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
¹⁰ Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
¹¹ Durrer Center for Cardiovascular Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands.
¹² Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.
¹³ Farr Institute of Health Informatics Research and Institute of Health Informatics, University College London, London, UK.

PMID: 28559929
PMCID: PMC5446754
DOI: 10.1186/s13040-017-0137-5

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

Vinicius Tragante et al. BioData Min. 2017.

. 2017 May 26:10:18.

doi: 10.1186/s13040-017-0137-5. eCollection 2017.

Authors

Affiliations

¹ Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
² Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
³ Departments of Medicine and Preventive Medicine, Boston University School of Medicine, Boston, MA USA.
⁴ Department of Epidemiology, University of Washington, Seattle, WA USA.
⁵ Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
⁶ Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
⁷ Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
⁸ Population Pharmacogenetics Group, University of Dundee, Dundee, UK.
⁹ Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
¹⁰ Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
¹¹ Durrer Center for Cardiovascular Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands.
¹² Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.
¹³ Farr Institute of Health Informatics Research and Institute of Health Informatics, University College London, London, UK.

PMID: 28559929
PMCID: PMC5446754
DOI: 10.1186/s13040-017-0137-5

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.

Keywords: Coronary artery disease; Gene set enrichment analyses; Heart failure.

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Figures

**Fig. 1**
Pipeline of a GSEA. Preprocessing is essential for all the input of the system

See this image and copyright information in PMC

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

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

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