中国科学院心理研究所王晶研究员课题组在全基因组关联学习（genome-wide association study, GWAS）研究中取得重要成果。课题组成功开发了基于通路（pathway）的GWAS数据网络分析平台——i-GSEA4GWAS。该平台用于鉴别与疾病表型相关的通路/基因集，以进一步研究和揭示疾病致病机理。
全基因组关联学习（GWAS）是一种对全基因组范围内的常见遗传多态性（主要是单核苷酸多态性-single nucleotide polymorphisms, SNPs）进行总体关联分析的方法，适用于包括精神疾病（mental disorder）在内的复杂疾病的研究。传统GWAS数据分析方法对SNP/基因独立的进行分析，忽略了复杂疾病的多基因联合效应。为解决上述问题，近年来基于通路的研究原则被引入到GWAS数据分析，检测包含多个基因的通路和性状的关联。基于上述观点，王晶课题组成功研究开发了基于通路的GWAS分析方法（i-GSEA）和工具，通过网络服务的方式供世界范围相关研究工作者使用（i-GSEA4GWAS，URL: ｈｔｔｐ://ｇｓｅａ4ｇｗａｓ.ｐｓｙｃｈ.ａｃ.ｃｎ）。课题组使用i-GSEA4GWAS对一种精神疾病——双向情感障碍（bipolar disorder）的GWAS数据进行了分析，并发现了新的可能的疾病相关通路/基因集。
该项研究得到了中国科学院心理研究所青年科学基金（O9CX115011）和北京市科学技术委员会北京市科技新星计划（A类）（2007A082）的资助。（生物谷Bioon.com）
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生物谷推荐原文出处：
Nucleic Acids Research doi:10.1093/nar/gkq324
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
Kunlin Zhang, Sijia Cui, Suhua Chang, Liuyan Zhang and Jing Wang*
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.
