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

基本信息

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
项目类别：
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

项目摘要

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）是研究遗传变异对基因组和转录水平上复杂疾病的影响的两种主要方法。它是针对RNA-Seq的，它正在迅速成为鉴定疾病中差异表达基因的强大工具。但是，由于基因法规的复杂性，仍然存在许多挑战。在此提案中，我们将统计数据，生物信息学和遗传学结合在一起，以开发新的分析策略，从而最大程度地利用GWAS和RNA-SEQ研究中的信息，以了解基本的复杂疾病，尤其是精神分裂症。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论。我们提出以下四个主要目的：（1）通过利用通过RNA测序衡量的功能信息来开发新的分析策略，以识别GWA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）通过利用基因表达研究遗传学的信息来开发新的分析策略，以在RNA-seq数据中识别具有富集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个大脑样本中产生了RNA-Seq数据，并获得了四个精神分裂症的主要GWAS数据集（ISC，Gain，Nongain，Nongain和Catie：总共6000例案例和6000个对照组）。该应用还将帮助我们完善目标1和2的策略。（4）开发用于检测疾病基因的计算工具，以及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，可以应用于具有可用RNA-Seq和GWAS数据集的任何复杂疾病。目标1和2的成功完成将为我们提供重要的GWA和RNA-SEQ数据集基因组分析的重要方法。 AIM 3的成功完成将为我们提供一系列优先的候选基因和途径，以实现对精神分裂症的未来验证。 AIM 4的成功完成将为使用GWAS和RNA-Seq研究复杂疾病的调查人员提供计算工具和用户友好的在线系统。