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Bioinformatics Advance Access published online on October 14, 2008
Bioinformatics, 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, University of Oslo, Oslo, Norway
4Department of Biostatistics, University of Oslo, Oslo, Norway

*To whom correspondence should be addressed. Dr. Bettina Kulle, E-mail: bkulle{at}medisin.uio.no


  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.

Associate Editor: Prof. Martin Bishop

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

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