全基因组关联分析（Genome-wide association study，GWAS）是一种对全基因组范围内的常见遗传变异基因总体关联分析的方法。GWAS为全面系统研究复杂疾病的遗传因素掀开了新的一页，为了解人类复杂疾病的发病机制提供了更多的线索。目前，科学家已经对糖尿病、冠心病、肺癌、前列腺癌、肥胖、精神病等多种复杂疾病进行了GWAS，并找到了疾病相关的易感位点。但是，大多数这样的位点对患病风险的贡献比较小，只能解释约10%的遗传作用，80%以上的遗传贡献遗失，这一现象被称为复杂疾病研究中“丢失的遗传性（missing heritability）”。如何挽回这种丢失的遗传性是当今复杂疾病遗传学研究的重要课题。
中国科学院上海生命科学研究院/上海交通大学医学院健康科学研究所中国科学院干细胞重点实验室孔祥银研究组博士研究生刘洋等人与国家人类基因组南方研究中心、浙江大学等单位合作，突破传统GWAS单位点分析的局限，建立了基于不同遗传位点相互作用的全基因组关联分析方法和程序（http://www.ihs.ac.cn/xykong/PIAM.zip），并利用公共GWAS数据，成功发现多个传统分析方法遗失的复杂疾病包括冠心病（coronary artery disease, CAD）、II型糖尿病（type 2 diabetes，T2D）、克罗恩病（Crohn’s disease，CD）等疾病的易感新位点。
此项研究不但揭示了不同位点之间的相互作用与患病风险可能存在相关性，而且加深了人类对复杂疾病遗传位点构架（genetic architecture）的了解。相关论文近日已在线发表于《公共科学图书馆?遗传学》（PLoS Genetics）。该杂志审稿人对此项研究做出高度评价：这是探索复杂疾病研究中“丢失的遗传性”一次成功而有意义的尝试。该项研究工作得到了国家科技部、国家自然科学基金委、中国科学院以及上海超级计算中心的支持。（生物谷Bioon.com）
生物谷推荐原文出处：
PLoS Genet 7(3): e1001338. doi:10.1371/journal.pgen.1001338
Genome-Wide Interaction-Based Association Analysis Identified Multiple New Susceptibility Loci for Common Diseases
Yang Liu1, Haiming Xu2, Suchao Chen3, Xianfeng Chen1, Zhenguo Zhang1, Zhihong Zhu2, Xueying Qin3, Landian Hu1, Jun Zhu2, Guo-Ping Zhao4, Xiangyin Kong1*
Abstract
Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named “pair-wise interaction-based association mapping” (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohn's disease, with a Bonferroni corrected P<0.05 (P = 0.039). This interaction was replicated with a pair of proxy linked loci (P = 0.013) on an independent dataset. Five other interactions had corrected P<0.5. We identified the allelic effect of a locus close to SLC7A13 for coronary artery disease. This was replicated with a linked locus on an independent dataset (P = 1.09×10?7). Through a local validation analysis that evaluated association signals, rather than locus-based associations, we found that several other regions showed association/interaction signals with nominal P<0.05. In conclusion, this study demonstrated that the GWIBA approach was successful for identifying novel loci, and the results provide new insights into the genetic architecture of common diseases. In addition, our PIAM program was capable of handling very large GWAS datasets that are likely to be produced in the future.
