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

Vinicius Tragante; Johannes M. I. H. Gho; Janine F. Felix; Ramachandran S. Vasan; Nicholas L. Smith; Benjamin F. Voight; Colin Palmer; Pim van der Harst; Jason H. Moore; Folkert W. Asselbergs; CHARGE Heart Failure Working Group

doi:10.1186/s13040-017-0137-5

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

Vinicius Tragante (Lead / Corresponding author), Johannes M. I. H. Gho, Janine F. Felix, Ramachandran S. Vasan, Nicholas L. Smith, Benjamin F. Voight, Colin Palmer, Pim van der Harst, Jason H. Moore, Folkert W. Asselbergs, CHARGE Heart Failure Working Group

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Abstract

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.

Original language	English
Article number	18
Pages (from-to)	1-11
Number of pages	11
Journal	BioData Mining
Volume	10
DOIs	https://doi.org/10.1186/s13040-017-0137-5
Publication status	Published - 26 May 2017

Keywords

Gene set enrichment analyses
Heart failure
Coronary artery disease

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10.1186/s13040-017-0137-5Licence: CC BY

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©The Author(s). 2017 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated
Final published version, 584 KBLicence: CC BY

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title = "Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype",

abstract = "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.",

keywords = "Gene set enrichment analyses , Heart failure , Coronary artery disease",

author = "Vinicius Tragante and Gho, {Johannes M. I. H.} and Felix, {Janine F.} and Vasan, {Ramachandran S.} and Smith, {Nicholas L.} and Voight, {Benjamin F.} and Colin Palmer and {van der Harst}, Pim and Moore, {Jason H.} and Asselbergs, {Folkert W.} and {CHARGE Heart Failure Working Group}",

note = "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).",

year = "2017",

month = may,

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doi = "10.1186/s13040-017-0137-5",

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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.

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

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