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

通过 RNA-seq 和 GWAS 数据绘制复杂疾病的遗传结构

• 批准号: 8217762
• 负责人: Lily Wang
• 金额: $ 40.15万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8217762
• 关键词: Accounting; Architecture; Bioinformatics; Biological; Biological Process; Brain; Candidate Disease Gene; Chromosome Mapping; Communities; Complex; DNA; Data; Data Analyses; Data Set; Development; Disease; Future; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Genes; Genetic; Genetic Polymorphism; Genetic Variation; Genomics; Human; Individual; Inherited; Lead; Length; Light; Linkage Disequilibrium; Measures; Medical Research; Methodology; Methods; Modeling; Molecular; Online Systems; Pathway interactions; Pattern; Phenotype; Play; Proteins; RNA; RNA Sequences; Research Institute; Research Personnel; Resources; Role; Sampling; Schizophrenia; Signal Transduction; Source; System; Technology; Testing; Transcript; Validation; Variant; Weight; base; computerized tools; disorder risk; flexibility; genetic analysis; genetic association; genetic variant; genome wide association study; improved; meetings; novel; programs; statistics; tool; transcriptomics; user-friendly

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.

描述（由申请人提供）：全基因组关联研究（GWAS）和RNA测序（RNA-Seq）是分别在基因组和转录组水平研究遗传变异对复杂疾病影响的两种主要方法。特别是对于RNA-Seq，它正迅速成为一种鉴定疾病中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。在这项提案中，我们结合联合收割机统计学，生物信息学和遗传学，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。我们的提案将是第一个整合GWAS和RNA-Seq数据的系统方法的方法开发。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通路。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，在RNA-Seq数据中识别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导向分析。(3)为了将目标1和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个大脑样本中生成了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）。这一应用还将帮助我们完善目标1和2中的战略。(4)开发用于检测疾病基因的计算工具，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，并可应用于任何具有可用RNA-Seq和GWAS数据集的复杂疾病。目标1和2的成功完成将为我们提供GWAS和RNA-Seq数据集的整合基因组分析的重要方法。目标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，供未来对精神分裂症进行验证。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。