Prof WANG Jing’s group achieved the important progress in their research about genome-wide association study (GWAS). They developed a web server for pathway-based GWAS data analysis - i-GSEA4GWAS. The web server is functional for identifying pathways/gene sets associated with traits to further study and reveal the disease pathogenesis.

Genome-wide association study (GWAS) is a routine approach to identify genes involved in human complex disease including mental illness. The regular GWAS analysis independently examines SNPs/genes and usually identifies only a small number of the most significant SNPs/genes. It ignores the combined effect of weaker/modest SNPs/genes, which leads to difficulties to explore biological function, mechanism and pathogenesis from a systems point of view.

To address this issue, pathway-based analysis has been introduced to GWAS to identify the correlation between pathways/gene sets and traits. Based on this principle, Prof Wang’s group implemented a pathway-based approach, GSEA. They further improved GSEA (i-GSEA) by emphasizing on pathways/gene sets consisting of high proportion of significant genes, and developed the i-GSEA4GWAS (improved GSEA for GWAS) web server (URL: http://gsea4gwas.psych.ac.cn/).Utilizing i-GSEA4GWAS to interpret the GWAS data of bipolar disorder, a mental illness, they found a new possible disease-related pathway/gene set.

The study was supported by Project for Young Scientists Fund, Institute of Psychology, Chinese Academy of Sciences (O9CX115011) and the Beijing New Star Project, Beijing Municipal Science & Technology Commission Foundatio (2007A082). The result has been published in Nucleic Acids Research (Impact Factor: 6.878).

Kunlin Zhang; Sijia Cui; Suhua Chang; Liuyan Zhang; Jing Wang. 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. Nucleic Acids Research 2010; doi: 10.1093/nar/gkq324