Mapping the Genetic Architecture of Complex Disease via RNA-seq and GWAS Data

描述（由申请人提供）：全基因组关联研究（GWAS）和RNA测序（RNA- seq）分别是在基因组和转录组水平上研究遗传变异对复杂疾病影响的两种主要方法。特别是RNA-Seq，它正迅速成为识别疾病中差异表达基因的有力工具；然而，由于基因调控的复杂性，许多挑战仍然存在。在本研究中，我们将统计学、生物信息学和遗传学结合起来，开发新的分析策略，最大限度地利用GWAS和RNA-Seq研究的信息，以了解复杂疾病，特别是精神分裂症的遗传结构。我们的建议将是集成GWAS和RNA-Seq数据的系统方法的第一个方法学开发。我们提出以下四个主要目标：(1)利用RNA测序测量的功能信息，开发新的分析策略，以识别GWAS中具有丰富关联信号的基因和途径。我们将这种方法定义为RNA-Seq辅助GWAS分析。(2)利用基因表达研究的遗传学信息，开发新的分析策略，以识别RNA-Seq数据中具有丰富关联信号的基因和途径。我们将这种方法定义为RNA-Seq定向分析。(3)为了将目标1和目标2中的方法应用于精神分裂症，我们从斯坦利医学研究所收集的82个脑样本中生成了RNA-Seq数据，并获得了精神分裂症的四个主要GWAS数据集（ISC、GAIN、nonGAIN和CATIE：总共超过6000例病例和6000例对照）。这个应用程序还将帮助我们改进目标1和目标2中的策略。(4)开发用于检测疾病基因、导致复杂疾病的途径的计算工具。这些工具将成为公共社区的有用资源，并可应用于具有可用RNA-Seq和GWAS数据集的任何复杂疾病。目标1和目标2的成功完成将为GWAS和RNA-Seq数据集的整合基因组分析提供重要的方法。Aim 3的成功完成将为我们提供一个优先候选基因和途径的列表，用于未来对精神分裂症的验证。Aim 4的成功完成将为使用GWAS和RNA-Seq研究复杂疾病的研究人员提供计算工具和用户友好的在线系统。

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) and RNA sequencing (RNA-Seq) are two major approaches for studying the effects of genetic variations on complex diseases at the genomic and transcriptomic levels, respectively. Specifically for RNA-Seq, it is rapidly emerging as a powerful tool for identifying differentially expressed genes in diseases; however, many challenges remain because of the complexity in gene regulations. In this proposal, we combine statistics, bioinformatics, and genetics to develop novel analytical strategies that maximally leverage information from both GWAS and RNA-Seq studies in order to understand the genetic architecture underlying complex diseases, especially schizophrenia. Our proposal will be the first methodology development for a systems approach that integrates GWAS and RNA-Seq data. We propose the following four major aims: (1) To develop novel analytical strategies to identify genes and pathways with enriched association signals in GWAS by leveraging functional information measured by RNA sequencing. We define this approach as RNA-Seq assisted GWAS analysis. (2) To develop novel analytical strategies to identify genes and pathways with enriched association signals in RNA-Seq data by leveraging information from genetics of gene expression studies. We define this approach as RNA-Seq oriented analysis. (3) To apply the methods in Aims 1 and 2 to schizophrenia, which we have generated RNA-Seq data from 82 brain samples collected from the Stanley Medical Research Institute and gained access to four major GWAS datasets for schizophrenia (ISC, GAIN, nonGAIN, and CATIE: a total of more than 6000 cases and 6000 controls). This application will also help us refine the strategies in Aims 1 and 2. (4) To develop computational tools for detecting disease genes, pathways that lead to complex diseases. These tools will become a useful resource for the public community and can be applied to any complex diseases with available RNA-Seq and GWAS datasets. The successful completions of Aims 1 and 2 will provide us with important methods for integrative genomic analysis of GWAS and RNA-Seq datasets. The successful completion of Aim 3 will provide us with a list of prioritized candidate genes and pathways for future validation on schizophrenia. The successful completion of Aim 4 will provide computational tools and a user-friendly online system for investigators who study complex diseases using GWAS and RNA-Seq.