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

描述(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水平上研究遗传变异对复杂疾病的影响的两种主要方法。特别是对于RNA-Seq，它正在迅速成为识别疾病差异表达基因的强大工具；然而，由于基因调控的复杂性，仍然存在许多挑战。在这项建议中，我们结合统计学、生物信息学和遗传学来开发新的分析策略，最大限度地利用来自Gwas和RNA-Seq研究的信息，以了解复杂疾病，特别是精神分裂症背后的遗传结构。我们的提案将是第一次对整合GWAS和RNA-Seq数据的系统方法进行方法学开发。我们提出了以下四个主要目标：(1)开发新的分析策略，通过利用RNA测序测量的功能信息来识别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辅助的GWAS分析。(2)开发新的分析策略，通过利用基因表达研究的遗传学信息来识别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面向RNA-Seq的分析。(3)将AIMS 1和AIMS 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数据库(ISC、GAIN、NONGAIN和CATIE：总共超过6000例和6000名对照)。这一应用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的计算工具，即导致复杂疾病的途径。这些工具将成为公共社区的有用资源，并可应用于拥有现有RNA-Seq和Gwas数据集的任何复杂疾病。AIMS 1和AIMS 2的成功完成将为我们提供对GWAS和RNA-Seq数据集进行综合基因组分析的重要方法。AIM 3的成功完成将为我们提供一份优先候选基因的清单，并为未来精神分裂症的验证提供途径。AIM 4的成功完成将为使用全球气候变化系统和RNA-Seq研究复杂疾病的研究人员提供计算工具和一个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.