繼成功開發(fā)基于通路的全基因組關(guān)聯(lián)研究（GWAS）數(shù)據(jù)網(wǎng)絡(luò)分析平臺i-GSEA4GWAS之后，中國科學(xué)院心理健康重點實驗室王晶研究員和張昆林助理研究員等研究者又開發(fā)了網(wǎng)絡(luò)分析平臺ICSNPathway（Identify candidate Causal SNPs and Pathways）,，實現(xiàn)了在一個分析框架下對GWAS數(shù)據(jù)的深入分析和綜合詮釋。該平臺的特色在于將連鎖不平衡分析和功能SNP注釋與基于通路的分析方法相結(jié)合，有效地利用GWAS數(shù)據(jù)鑒定出與復(fù)雜疾病/表型相關(guān)的致病SNPs（causal SNPs）及通路,，為后續(xù)的生物機制研究提供合理的假說和依據(jù),。
全基因組關(guān)聯(lián)研究（Genome-wide association study,，GWAS）已被廣泛應(yīng)用于人類復(fù)雜疾病/表型相關(guān)遺傳位點的發(fā)現(xiàn)與鑒定,。然而,，龐大的數(shù)據(jù)量（百萬以上個多態(tài)性位點,，數(shù)千至上萬個樣本）為GWAS數(shù)據(jù)的解析和詮釋帶來了諸多問題,。盡管已經(jīng)有多個具有較強統(tǒng)計顯著性的多態(tài)性位點被發(fā)現(xiàn)，但鑒定出真正致病的SNPs并提供影響表型的證據(jù)仍然是GWAS數(shù)據(jù)詮釋的重要挑戰(zhàn)之一,。目前的GWAS研究大多只關(guān)注統(tǒng)計顯著性最強的一些SNPs,，而缺少代表生物機制的通路信息的支持?；谕返姆治龇椒ǎ≒athway-based analysis,，PBA）彌補了這一不足,，但現(xiàn)有的方法忽略了SNPs的功能性，造成對致病因子的詮釋不足,。
ICSNPathway通過整合連鎖不平衡分析,、功能SNP注釋及基于通路的分析方法，實現(xiàn)了對SNP功能的進一步挖掘和有效利用,，從而鑒定出候選致病SNPs以及相關(guān)的代謝通路,。作為一個開放的網(wǎng)絡(luò)分析平臺，ICSNPathway為相關(guān)的研究者提供免費的服務(wù),，幫助研究者對GWAS數(shù)據(jù)進行深入詮釋,，產(chǎn)生從SNP到基因再到通路的假說，架起了GWAS與疾病機理研究的橋梁,。
該項研究得到了中科院知識創(chuàng)新工程項目（KSCX2-EW-J-8）和中科院心理研究所青年科學(xué)基金（O9CX115011）的資助,。相關(guān)研究成果發(fā)表在生物信息學(xué)期刊《核酸研究》（Nucleic Acids Research） 上。（生物谷 Bioon.com）
doi:10.1093/nar/gkr391
PMC:
PMID:
ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework
Kunlin Zhang1, Suhua Chang1,2, Sijia Cui1,2, Liyuan Guo1, Liuyan Zhang1,2 and Jing Wang1,*
Genome-wide association study (GWAS) is widely utilized to identify genes involved in human complex disease or some other trait. One key challenge for GWAS data interpretation is to identify causal SNPs and provide profound evidence on how they affect the trait. Currently, researches are focusing on identification of candidate causal variants from the most significant SNPs of GWAS, while there is lack of support on biological mechanisms as represented by pathways. Although pathway-based analysis (PBA) has been designed to identify disease-related pathways by analyzing the full list of SNPs from GWAS, it does not emphasize on interpreting causal SNPs. To our knowledge, so far there is no web server available to solve the challenge for GWAS data interpretation within one analytical framework. ICSNPathway is developed to identify candidate causal SNPs and their corresponding candidate causal pathways from GWAS by integrating linkage disequilibrium (LD) analysis, functional SNP annotation and PBA. ICSNPathway provides a feasible solution to bridge the gap between GWAS and disease mechanism study by generating hypothesis of SNP → gene → pathway(s). The ICSNPathway server is freely available at http://icsnpathway.psych.ac.cn/.
