继成功开发基于通路的全基因组关联研究（GWAS）数据网络分析平台i-GSEA4GWAS之后，中国科学院心理健康重点实验室王晶研究员和张昆林助理研究员等研究者又开发了网络分析平台ICSNPathway（Identify candidate Causal SNPs and Pathways），实现了在一个分析框架下对GWAS数据的深入分析和综合诠释。该平台的特色在于将连锁不平衡分析和功能SNP注释与基于通路的分析方法相结合，有效地利用GWAS数据鉴定出与复杂疾病/表型相关的致病SNPs（causal SNPs）及通路，为后续的生物机制研究提供合理的假说和依据。
全基因组关联研究（Genome-wide association study，GWAS）已被广泛应用于人类复杂疾病/表型相关遗传位点的发现与鉴定。然而，庞大的数据量（百万以上个多态性位点，数千至上万个样本）为GWAS数据的解析和诠释带来了诸多问题。尽管已经有多个具有较强统计显著性的多态性位点被发现，但鉴定出真正致病的SNPs并提供影响表型的证据仍然是GWAS数据诠释的重要挑战之一。目前的GWAS研究大多只关注统计显著性最强的一些SNPs，而缺少代表生物机制的通路信息的支持。基于通路的分析方法（Pathway-based analysis，PBA）弥补了这一不足，但现有的方法忽略了SNPs的功能性，造成对致病因子的诠释不足。
ICSNPathway通过整合连锁不平衡分析、功能SNP注释及基于通路的分析方法，实现了对SNP功能的进一步挖掘和有效利用，从而鉴定出候选致病SNPs以及相关的代谢通路。作为一个开放的网络分析平台，ICSNPathway为相关的研究者提供免费的服务，帮助研究者对GWAS数据进行深入诠释，产生从SNP到基因再到通路的假说，架起了GWAS与疾病机理研究的桥梁。
该项研究得到了中科院知识创新工程项目（KSCX2-EW-J-8）和中科院心理研究所青年科学基金（O9CX115011）的资助。相关研究成果发表在生物信息学期刊《核酸研究》（Nucleic Acids Research） 上。（生物谷 Bioon.com）
doi:10.1093/nar/gkr391
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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/.
