Tragante, V;
Gho, JMIH;
Felix, JF;
Vasan, RS;
Smith, NL;
Voight, BF;
Palmer, C;
... Asselbergs, FW; + view all
(2017)
Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype.
BioData Mining
, 10
, Article 18. 10.1186/s13040-017-0137-5.
Preview |
Text
s13040-017-0137-5.pdf - Published Version Download (597kB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s13040-017-0137-5 |
| Publisher version: | https://doi.org/10.1186/s13040-017-0137-5 |
| Language: | English |
| Additional information: | © 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. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Mathematical & Computational Biology, Gene Set Enrichment Analyses, Heart Failure, Coronary Artery Disease, Urinary Albumin Excretion, Genome-Wide Association, Expression Profiles, Biological Pathways, Population, Risk, Classification, Identification, Knowledgebase, Epidemiology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1570521 |
Archive Staff Only
 |
View Item |
