i-GSEA4GWAS: a web server for identification of pathways/gene sets associated with traits by applying an improved gene set enrichment analysis to genome-wide association study

Zhang, Kunlin; Cui, Sijia; Chang, Suhua; Zhang, Liuyan; Wang, Jing

i-GSEA employs SNP label permutation to correct gene variation to reduce the bias due to different genes with different number of mapped SNPs, which is ignored by the non-randomization approach such as the segmentation test (33) as implemented in the GeSBAP web server (10) . This correction ensures to identify gene sets consisting of non-random high-association genes with biological plausibility instead of random high-association genes with large numbers of mapped SNPs. Furthermore, the key issue of i-GSEA, focusing on pathways/gene sets with high proportion of significant genes to detect combinations of modest effects, greatly improves sensitivity. Let n denote the number of pathways with FDR < 0.05 and m represent the number of pathways with both FDR < 0.05 and references to support, our comparison study between i-GSEA and GSEA shows, in the HIV-1 host control and bipolar disorder study as described above, n = 7 and m = 6 for i-GSEA, while n = 2 and m = 2 for GSEA. Further comparison by using the other six Wellcome Trust Case Control Consortium GWASs except bipolar disorder (2) obtained the result that n = 33 and m = 14 for i-GSEA, while n = 2, m = 1 for GSEA. In addition, the pathways identified (FDR < 0.05) by i-GSEA include all the pathways identified by GSEA in all the above comparisons. These show that i-GSEA has improved sensitivity in comparison to GSEA. Our data examples also show that i-GSEA4GWAS obtains very meaningful results with different GWAS designs (quantitative trait and case/ control) and different genotyping platforms (Illumina and Affymetrix).