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Bioinformatics Advance Access originally published online on October 14, 2008
Bioinformatics 2008 24(23):2784-2785; doi:10.1093/bioinformatics/btn516
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies

Marit Holden 1, Shiwei Deng 2, Leszek Wojnowski 2 and Bettina Kulle 3,4,*

1Norwegian Computing Center, Oslo, Norway, 2Department of Pharmacology, University of Mainz, Mainz, Germany, 3Epi-Gen, Faculty Division Akershus University Hospital and 4Department of Biostatistics, University of Oslo, Oslo, Norway

*To whom correspondence should be addressed.


  Abstract

The power of genome-wide SNP association studies is limited, among others, by the large number of false positive test results. To provide a remedy, we combined SNP association analysis with the pathway-driven gene set enrichment analysis (GSEA), recently developed to facilitate handling of genome-wide gene expression data. The resulting GSEA-SNP method rests on the assumption that SNPs underlying a disease phenotype are enriched in genes constituting a signaling pathway or those with a common regulation. Besides improving power for association mapping, GSEA-SNP may facilitate the identification of disease-associated SNPs and pathways, as well as the understanding of the underlying biological mechanisms. GSEA-SNP may also help to identify markers with weak effects, undetectable in association studies without pathway consideration. The program is freely available and can be downloaded from our website.

Contact: bkulle{at}medisin.uio.no

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Martin Bishop

Received on August 7, 2008; revised on October 2, 2008; accepted on October 2, 2008

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