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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

主要研究者/项目负责人（末、首、中）：赵忠明项目摘要全基因组关联研究（GWAS）和RNA测序（RNA-Seq）是两种主要的方法，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，它正在迅速成为一种强大的工具，用于识别差异然而，由于基因表达的复杂性，规定在这个提议中，我们将联合收割机统计学、生物信息学和遗传学结合起来，开发新的分析方法，最大限度地利用来自GWAS和RNA-Seq研究的信息，复杂疾病的遗传结构，尤其是精神分裂症。我们的提案将是第一个为整合GWAS和RNA-Seq数据的系统方法开发方法。我们建议以下四个主要目标：（1）开发新的分析策略，以确定基因和途径，通过利用RNA测序测量的功能信息，丰富了GWAS中的关联信号。我们将该方法定义为RNA-Seq辅助的GWAS分析。(2)开发新的分析策略，通过利用来自以下方面的信息，在RNA-Seq数据中鉴定具有丰富关联信号的基因和途径：基因表达研究的遗传学。我们将这种方法定义为RNA-Seq导向分析。(3)应用我们已经从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，并获得了四个主要的GWAS数据集，精神分裂症（ISC、GAIN、nonGAIN和CATIE：总计超过6000例病例和6000例对照）。这应用这些技术还将帮助我们完善目标1和2中的战略。(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适用于公共社区，并可应用于任何复杂的疾病与可用的RNA-Seq和GWAS 数据集。目标1和目标2的成功完成将为我们提供重要的方法， GWAS和RNA-Seq数据集的基因组分析。目标3的成功完成，将为我们提供一个优先候选基因和路径的列表，用于未来对精神分裂症的验证。成功目标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