定位复杂疾病的易感基因、解析复杂疾病的遗传调控机制对于复杂疾病的诊断与治疗具有重要指导意义。针对目前主流的全基因组关联分析（GWAS）依靠单一数据源无法准确定位易感基因位置的问题，本项目拟采用GWAS与eQTL分析相结合的方式进行复杂疾病易感基因定位及其分子机制解析。首先，通过引入线性混合模型并结合稀疏组优化方法，建立GWAS框架下复杂疾病多位点关联分析模型；然后，提出基于因子分解机模型的上位效应检测方法，进一步扩充复杂疾病关联变异信息；随后，采用多任务学习框架与稀疏重叠组优化方法相结合的方式，建立eQTL分析模型；最后，对GWAS与eQTL结果重叠区域进行易感基因共定位检测，并通过基因共表达网络、功能富集分析等技术实现易感基因功能预测与分析。本项目对于发展信息科学与生物医学交叉的多组学数据融合分析方法具有重要的理论研究意义，同时在探索复杂疾病发生发展的遗传调控机制方面具有潜在的应用价值。

The localization of the susceptibility genes and the analysis of gene regulation mechanisms are important for the diagnosis and treatment of complex diseases. At the moment, mainstream GWAS techniques suffer from the difficulty of localization susceptibility genes accurately due to its reliance on a single source of information. To solve this issue, in this project we plan to propose a new framework that combines GWAS with eQTL in the studies of susceptibility genes and its molecular regulation mechanism. As a first step, we plan to build a multivariate GWAS model for complex diseases based on a linear mixed model and sparse group regularization techniques. Next, to expand the association variants information, we will propose a factorization machine based method for the detection of epistasis. Then, we will propose an eQTL analysis model by applying the multi-task learning framework and a sparse overlapping group optimization method. Lastly, we will vary out colocation tests for susceptibility genes across the overlaps of the results of GWAS and eQTL and make predictions on the function of these genes via techniques such as co-expression gene network analysis and GO functional enrichment analysis. Our project will make important theoretical contributions to multi-omics data analysis which is an interdisciplinary filed between computer sciences and biomedical science and will have potential applications on the investigation of the genetic mechanisms behind the onset and progression of complex diseases.