复杂疾病的全基因组SNP互作网络构建与分析

项目名称： 复杂疾病的全基因组SNP互作网络构建与分析

项目编号： No.61502272

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 计算机科学学科

项目作者： 尚军亮

作者单位： 曲阜师范大学

项目金额： 20万元

中文摘要： 复杂疾病往往是由多种因素共同作用导致的。全基因组SNP互作模式识别是解析复杂疾病遗传机理的重要方法，然而从SNP互作网络构建这一角度识别互作模式的研究当前还比较少。项目以仿真数据和特定复杂疾病（年龄相关性黄斑变性和卵巢癌）全基因组SNP数据为基础，致力于构建全基因组SNP互作网络并利用网络分析方法识别互作模式。主要研究内容有关联测度的设计、SNP互作网络的构建、互作模式的识别和结果的生物学解释。研究方法包括基于共信息理论的显著并稳定测度、基于超图理论的带权超图及其等价图、网络分析方法（包括顶点度、组合顶点间距和网络置换检验等）、GO和Pathway富集分析，其中显著并稳定测度和带权超图是项目的方法创新。项目成果将有助于大数据挖掘、网络构建分析和互作模式识别等问题的研究，也有利于解释年龄相关性黄斑变性和卵巢癌的遗传机理，在疾病早期诊断、个性化治疗及药物研制等方面有较好的社会效益和经济效益。

中文关键词： 复杂疾病；全基因组关联研究；单核苷酸多态性；遗传互作网络；基因交互作用

英文摘要： Complex diseases are normally caused by interaction effects of multiple factors. Genome-wide SNP interaction pattern detection is an important method to uncover underlying genetic mechanisms of complex diseases. Nevertheless, currently there are few studies dealing with the detection of SNP interaction patterns from the viewpoint of SNP network construction. Our project, based on simulation data and real genome-wide SNP data of specific complex diseases, namely, age-related macular degeneration and ovarian cancer, focuses on detecting interaction patterns using the network analysis methods from the constructed SNP interaction networks. Main research contents of the project consist of four parts. They are the design of association measures, the construction of SNP interaction networks, the detection of interaction patterns and the biological explanation of detection results. The research methods to be adopted mainly include the significant and stable association measures based on the co-information theory, the weighted hyper-graph and its corresponding equivalent graph based on the hyper-graph theory, network analysis methods (such as the degree of each vertex, the distance of a combination of several vertices, the network permutation test, and many others), gene ontology/pathway enrichment analysis. Among them, the significant and stable association measures, as well as the weighted hyper-graph and its corresponding equivalent graph, are innovations of methods of our project. Expected results will contribute to the research of big data mining, network construction and interaction pattern recognition, and also help to explain underlying genetic mechanisms of age-related macular degeneration and ovarian cancer. Hence, our project and its results have good social and economic benefits in the areas of prevention and early warning, early diagnosis, personal treatment, drug development, and so on.

英文关键词： Complex Diseases;Genome-Wide Association Study;Single Nucleotide Polymorphism;Genetic Interaction Network;Gene Interaction