面向人类复杂疾病的eQTL模块挖掘及其meta分析方法研究

项目名称： 面向人类复杂疾病的eQTL模块挖掘及其meta分析方法研究

项目编号： No.61300116

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 李晋

作者单位： 哈尔滨医科大学

项目金额： 23万元

中文摘要： 人类复杂疾病风险位点识别是近些年的研究热点之一，表达数量性状位点（eQTL）定位将基因表达水平作为遗传变异和临床表型之间的中间分子表型，为研究复杂疾病风险位点提供了一种新途径。本项目针对eQTL模块挖掘中存在的eQTL模块重叠和多数据整合问题，重点研究重叠eQTL模型构建和meta分析方法。首先，提出有重叠eQTL模块模型，并发展其统计挖掘算法。然后，估计eQTL分析所需的合理样本量，并对不同研究的SNP、基因表达数据进行整合，构建单位点和多位点meta-eQTL模型。最后，结合GO、KEGG、OMIM、GWAS结果，系统分析组织特异和共有eQTL的生物学功能。本项目对开拓新的生物信息学方法，研究统计学、信息科学与生命科学的结合都具有重要的理论意义，并对人类复杂疾病风险位点识别具有潜在的应用价值。

中文关键词： 复杂疾病；表达数量性状位点；全基因组关联分析；基因-基因互作；功能富集分析

英文摘要： It is one of the hot research topics in recent years that the identification of the human complex disease risk loci. Expression quantitative trait loci(eQTL) mapping takes the level of gene expression as an intermediate molecular phenotype between genetic variation and clinical phenotype, and it provides a new way for the study of complex disease risk loci identification. For the problems of overlap in eQTL module mining and multiple data integration, this project focuses on the study of overlapping eQTL model construction and meta analysis method. Firstly, we propose an overlapping eQTL module model and develop its statistical mining algorithms. Next, we estimate the reasonable sample size required for eQTL analysis and integrate SNP and gene expression data from different studies to build the single locus and multilocus meta-eQTL models. Finally, combined with GO, KEGG, OMIM and GWAS results, we perform a systematic functional analysis for the tissue-specific and shared eQTLs. This project will be significant to exploiting the new bioinformatics methods, incorporating statistics and information science into the life science, which can be applied to the identification of human complex disease risk loci.

英文关键词： Complex Diseases；Expression Quantitative Trait Loci (eQTL)；Genome-Wide Association Study (GWAS)；Gene-Gene Interaction (GGI)；Functional Enrichment Analysis