全基因組關(guān)聯(lián)分析（Genome-wide association study,，GWAS）是一種對全基因組范圍內(nèi)的常見遺傳變異基因總體關(guān)聯(lián)分析的方法,。GWAS為全面系統(tǒng)研究復(fù)雜疾病的遺傳因素掀開了新的一頁，為了解人類復(fù)雜疾病的發(fā)病機(jī)制提供了更多的線索,。目前,，科學(xué)家已經(jīng)對糖尿病、冠心病,、肺癌、前列腺癌,、肥胖,、精神病等多種復(fù)雜疾病進(jìn)行了GWAS，并找到了疾病相關(guān)的易感位點,。但是,，大多數(shù)這樣的位點對患病風(fēng)險的貢獻(xiàn)比較小，只能解釋約10%的遺傳作用，80%以上的遺傳貢獻(xiàn)遺失,，這一現(xiàn)象被稱為復(fù)雜疾病研究中“丟失的遺傳性（missing heritability）”,。如何挽回這種丟失的遺傳性是當(dāng)今復(fù)雜疾病遺傳學(xué)研究的重要課題。
中國科學(xué)院上海生命科學(xué)研究院/上海交通大學(xué)醫(yī)學(xué)院健康科學(xué)研究所中國科學(xué)院干細(xì)胞重點實驗室孔祥銀研究組博士研究生劉洋等人與國家人類基因組南方研究中心,、浙江大學(xué)等單位合作,，突破傳統(tǒng)GWAS單位點分析的局限，建立了基于不同遺傳位點相互作用的全基因組關(guān)聯(lián)分析方法和程序（http://www.ihs.ac.cn/xykong/PIAM.zip）,，并利用公共GWAS數(shù)據(jù),，成功發(fā)現(xiàn)多個傳統(tǒng)分析方法遺失的復(fù)雜疾病包括冠心病（coronary artery disease, CAD）,、II型糖尿?。╰ype 2 diabetes，T2D）,、克羅恩?。–rohn’s disease，CD）等疾病的易感新位點,。
此項研究不但揭示了不同位點之間的相互作用與患病風(fēng)險可能存在相關(guān)性,，而且加深了人類對復(fù)雜疾病遺傳位點構(gòu)架（genetic architecture）的了解。相關(guān)論文近日已在線發(fā)表于《公共科學(xué)圖書館?遺傳學(xué)》（PLoS Genetics）,。該雜志審稿人對此項研究做出高度評價：這是探索復(fù)雜疾病研究中“丟失的遺傳性”一次成功而有意義的嘗試,。該項研究工作得到了國家科技部、國家自然科學(xué)基金委,、中國科學(xué)院以及上海超級計算中心的支持,。（生物谷Bioon.com）
生物谷推薦原文出處：
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.
