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.
Genome-wide association study (GWAS) is nowadays widely used to identify genes involved in human complex disease. The standard GWAS analysis examines SNPs/genes independently and identifies only a number of the most significant SNPs. It ignores the combined effect of weaker SNPs/genes, which leads to difficulties to explore biological function and mechanism from a systems point of view. Although gene set enrichment analysis (GSEA) has been introduced to GWAS to overcome these limitations by identifying the correlation between pathways/gene sets and traits, the heavy dependence on genotype data, which is not easily available for most published GWAS investigations, has led to limited application of it. In order to perform GSEA on a simple list of GWAS SNP P-values, we implemented GSEA by using SNP label permutation. We further improved GSEA (i-GSEA) by focusing on pathways/gene sets with high proportion of significant genes. To provide researchers an open platform to analyze GWAS data, we developed the i-GSEA4GWAS (improved GSEA for GWAS) web server. i-GSEA4GWAS implements the i-GSEA approach and aims to provide new insights in complex disease studies. i-GSEA4GWAS is freely available at http://gsea4gwas.psych.ac.cn/.
Zhang
Kunlin
K
Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 100101, Beijing, China.
Cui
Sijia
S
Chang
Suhua
S
Zhang
Liuyan
L
Wang
Jing
J
D000465:Algorithms; D004194:Disease / Q000235:genetics; D005796:Genes; D055106:Genome-Wide Association Study; D006801:Humans; D020407:Internet; D020641:Polymorphism, Single Nucleotide; D012984:Software; D014584:User-Computer Interface