GSEA-SNP: Applying Gene Set Enrichment Analysis to SNP data from genome-wide association studies

Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). The original GSEA software and method was developed for expression data and is implemented in R. In the paper "GSEA-SNP: Applying Gene Set Enrichment Analysis to SNP data from genome-wide association studies" by Marit Holden, Shiwei Deng, Leszek Wojnowski and Bettina Kulle, GSEA is adapted to SNP data by extending the original GSEA code. In the paper, the adapted method is called GSEA-SNP.

Implementation of the GSEA-SNP method

The introduced GSEA-SNP method is implemented in R by extending the original GSEA code. There are mainly two changes/extensions of the original code: Implementation of data-handling procedures for SNP data; and implementation of association tests for SNP data. The gene sets used for testing were downloaded together with the original GSEA software from http://www.broad.mit.edu/gsea/.

The GSEA-SNP program can be downloaded as a zip file or as a tar file.

The Cochrane-Armitage trend-test statistic

A description of how to compute the Cochrane-Armitage trend-test statistic is given here

GSEA-SNP test results for a case-control data set

The GSEA-SNP method was applied to a SNP data set derived from a nested case-control study as described in the supplementary data of the the paper about GSEA-SNP. Detailed results are found here.

Result of analyzing the SNPs one by one

Besides analysing the SNP data using the GSEA-SNP method, the SNPs have been analyzed one by one. Detailed results are found here.

Acknowledgement

We, the authors of the paper about GSEA-SNP, would like to thank the GSEA team for the possibility to modify the original GSEA code and publish it as a companion of our paper.