中國(guó)科學(xué)院心理研究所王晶研究員課題組在全基因組關(guān)聯(lián)學(xué)習(xí)（genome-wide association study, GWAS）研究中取得重要成果。課題組成功開(kāi)發(fā)了基于通路（pathway）的GWAS數(shù)據(jù)網(wǎng)絡(luò)分析平臺(tái)——i-GSEA4GWAS,。該平臺(tái)用于鑒別與疾病表型相關(guān)的通路/基因集,，以進(jìn)一步研究和揭示疾病致病機(jī)理。
全基因組關(guān)聯(lián)學(xué)習(xí)（GWAS）是一種對(duì)全基因組范圍內(nèi)的常見(jiàn)遺傳多態(tài)性（主要是單核苷酸多態(tài)性-single nucleotide polymorphisms, SNPs）進(jìn)行總體關(guān)聯(lián)分析的方法,，適用于包括精神疾?。╩ental disorder）在內(nèi)的復(fù)雜疾病的研究。傳統(tǒng)GWAS數(shù)據(jù)分析方法對(duì)SNP/基因獨(dú)立的進(jìn)行分析,，忽略了復(fù)雜疾病的多基因聯(lián)合效應(yīng),。為解決上述問(wèn)題，近年來(lái)基于通路的研究原則被引入到GWAS數(shù)據(jù)分析,，檢測(cè)包含多個(gè)基因的通路和性狀的關(guān)聯(lián),。基于上述觀點(diǎn),，王晶課題組成功研究開(kāi)發(fā)了基于通路的GWAS分析方法（i-GSEA）和工具,，通過(guò)網(wǎng)絡(luò)服務(wù)的方式供世界范圍相關(guān)研究工作者使用（i-GSEA4GWAS，URL: ｈｔｔｐ://ｇｓｅａ4ｇｗａｓ.ｐｓｙｃｈ.ａｃ.ｃｎ）,。課題組使用i-GSEA4GWAS對(duì)一種精神疾病——雙向情感障礙（bipolar disorder）的GWAS數(shù)據(jù)進(jìn)行了分析,，并發(fā)現(xiàn)了新的可能的疾病相關(guān)通路/基因集。
該項(xiàng)研究得到了中國(guó)科學(xué)院心理研究所青年科學(xué)基金（O9CX115011）和北京市科學(xué)技術(shù)委員會(huì)北京市科技新星計(jì)劃（A類）（2007A082）的資助。（生物谷Bioon.com）
關(guān)于GWAS的更多閱讀
高燒的GWAS——生物谷盤點(diǎn)2009
Nature Genetics：發(fā)現(xiàn)牛皮癬易感基因
Nature Genetics：漢族人紅斑 狼瘡易感基因
AJHG：中國(guó)人基因差異研究
全基因組關(guān)聯(lián)分析 費(fèi)力不討好,？
生物谷推薦原文出處：
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.
