However, GSEA usually depends on genotype data, which is not easily available for most published GWAS investigations. This leads to a limited application of it, especially application in the secondary analysis of published GWASs to best interpret the remaining long list of GWAS data from a systems point of view. In order to perform GSEA on easily available GWAS data, mainly SNP P-values, we implemented GSEA by using SNP label permutation instead of phenotype label permutation to analyze P-values. We further improved the GSEA (i-GSEA) by focusing on pathways/gene sets with high proportions of significant genes instead of relying only on the total significance coming from either a few or many significant genes. Our study shows that i-GSEA has the improved sensitivity to identify pathways/gene sets representing combined effects of possibly modest SNPs/genes.
