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

通过 RNA-seq 和 GWAS 绘制复杂疾病的遗传结构

基本信息

批准号：
9212507
负责人：
Zhongming Zhao
金额：
$ 23.53万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-03-01 至 2016-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9212507
关键词：
Mapping Genetic Architecture Complex Disease

项目摘要

Principal Investigator/Program Director (Last, first, middle): Zhao, Zhongming Project Summary 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. Project Description Page 6

首席研究员/项目主任（后、一、中）：赵忠明项目概要全基因组关联研究 (GWAS) 和 RNA 测序 (RNA-Seq) 是两种主要方法在基因组和转录组水平上研究遗传变异对复杂疾病的影响，分别。特别是对于 RNA-Seq，它正在迅速成为差异识别的强大工具疾病中表达的基因；然而，由于基因的复杂性，仍然存在许多挑战法规。在这个提案中，我们结合统计学、生物信息学和遗传学来开发新的分析方法最大限度地利用 GWAS 和 RNA-Seq 研究的信息来理解的策略复杂疾病（尤其是精神分裂症）背后的遗传结构。我们的建议将是第一个集成 GWAS 和 RNA-Seq 数据的系统方法的方法开发。我们建议以下四个主要目标：（1）开发新的分析策略来识别基因和通路利用 RNA 测序测量的功能信息丰富了 GWAS 中的关联信号。我们将这种方法定义为 RNA-Seq 辅助 GWAS 分析。 (2) 制定新颖的分析策略通过利用来自 RNA-Seq 数据的信息来识别具有丰富关联信号的基因和通路基因表达研究的遗传学。我们将这种方法定义为面向 RNA-Seq 的分析。 (3) 申请目标 1 和 2 中的方法用于治疗精神分裂症，我们从 82 个大脑样本中生成了 RNA-Seq 数据从斯坦利医学研究所收集并获得了四个主要 GWAS 数据集的访问权限精神分裂症（ISC、GAIN、nonGAIN 和 CATIE：总共超过 6000 例病例和 6000 例对照）。这应用程序还将帮助我们完善目标 1 和 2 中的策略。 (4) 开发计算工具检测疾病基因、导致复杂疾病的途径。这些工具将成为有用的资源面向公共社区，可通过可用的 RNA-Seq 和 GWAS 应用于任何复杂疾病数据集。目标1和2的成功完成将为我们提供整合的重要方法 GWAS 和 RNA-Seq 数据集的基因组分析。目标 3 的成功完成将为我们提供用于未来验证精神分裂症的优先候选基因和途径列表。成功者目标 4 的完成将为研究人员提供计算工具和用户友好的在线系统使用 GWAS 和 RNA-Seq 研究复杂疾病。项目描述第6页

项目成果

期刊论文数量（35）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Concordance of copy number loss and down-regulation of tumor suppressor genes: a pan-cancer study.

DOI：
10.1186/s12864-016-2904-y
发表时间：
2016-08-22
期刊：
BMC genomics
影响因子：
4.4
作者：
Zhao M;Zhao Z
通讯作者：
Zhao Z

Distinct and competitive regulatory patterns of tumor suppressor genes and oncogenes in ovarian cancer.

DOI：
10.1371/journal.pone.0044175
发表时间：
2012
期刊：
PloS one
影响因子：
3.7
作者：
Zhao M;Sun J;Zhao Z
通讯作者：
Zhao Z

Integrative pathway analysis of genome-wide association studies and gene expression data in prostate cancer.

DOI：
10.1186/1752-0509-6-s3-s13
发表时间：
2012
期刊：
BMC systems biology
影响因子：
0
作者：
Jia P;Liu Y;Zhao Z
通讯作者：
Zhao Z

Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach.

DOI：
10.1186/s12918-016-0309-9
发表时间：
2016-08-26
期刊：
BMC systems biology
影响因子：
0
作者：
Cheng F;Liu C;Shen B;Zhao Z
通讯作者：
Zhao Z

Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia.

DOI：
10.1371/journal.pcbi.1002587
发表时间：
2012
期刊：
PLoS computational biology
影响因子：
4.3
作者：
Jia P;Wang L;Fanous AH;Pato CN;Edwards TL;International Schizophrenia Consortium;Zhao Z
通讯作者：
Zhao Z