Integrative Modeling of Gene Expression into GWAS of Glioma

神经胶质瘤 GWAS 中基因表达的整合建模

• 批准号: 8995455
• 负责人: Yen-Tsung Huang
• 金额: $ 4.06万
• 依托单位: TUFTS UNIVERSITY BOSTON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8995455
• 关键词: Integrative; Modeling; Gene; Expression; into

DESCRIPTION (provided by applicant): Glioma accounts for 80% of all malignant brain tumor and produces enormous clinical and public health burden due to its poor prognosis. Given the lack of known causes for glioma, understanding genetic susceptibility might provide new insights and opportunities for progress in unraveling the biological mechanisms behind this fatal cancer. Genome-wide association studies (GWAS) constitute a popular approach for investigating the association of single nucleotide polymorphisms (SNPs) with disease. On the other hand, studies examining how genetic variants modify gene expression in tissue (i.e., quantitative trait loci (eQTL) studies) focus on the molecular quantitative trait. As the traditionl GWAS analysis is subject to power loss due to its agnostic approach, new strategies are required to identify additional and scientifically meaningful susceptibility loci of glioma risk. Hre we propose to integrate eQTL studies to more powerfully test the SNP effect on disease in GWAS when eQTL studies and GWAS are conducted among different subjects. With a regression model for the joint effect of SNPs and gene expression on disease risk, we have developed an efficient testing procedure for the overall effect of an eQTL SNP set in a gene or a pathway and illustrated its utility in numerical simulation studies and an asthma study. We will pursue the integrated analysis with three specific study aims. In Aim 1, we will conduct eQTL analyses using genome-wide SNP and expression data collected from post- mortem brain tissue obtained from neurologically normal subjects. In Aim 2, we will first form eQTL SNP sets in a gene or an immunomodulating pathway identified from Aim 1 and then conduct eQTL SNP-set analyses to investigate the association of eQTL SNPs with the risk of glioma using the publicly available GWAS data of the glioma risk. In Aim 3, we will perform integrated analyses of the SNP-set of a gene identified in Aim 2 and its expression value to assess the gene-based effect on the risk of glioma, either through a direct effect of eQTL SNPs or an indirect effect mediated by gene expression. The goal of this project is to build a new framework of conducting and analyzing a GWAS and identify new susceptibility loci for glioma. As different genomic data (i.e., SNPs and gene expression) are integrated in the analysis, we would expect a more statistical power to detect the disease-driving susceptibility loci than the SNP-only analysis. Furthermore, the results from our eQTL-integrated approach will also be more biologically meaningful and interpretable than the conventional agnostic GWAS because eQTL SNPs are more likely to be functional and the eQTL effect on the disease risk is explicitly modeled.

描述（由申请人提供）：胶质瘤占所有恶性脑肿瘤的80%，由于其预后不良，产生了巨大的临床和公共卫生负担。由于缺乏已知的神经胶质瘤原因，了解遗传易感性可能会为解开这种致命癌症背后的生物学机制提供新的见解和机会。全基因组关联研究（GWAS）是研究单核苷酸多态性（SNP）与疾病关联的流行方法。另一方面，研究遗传变异如何改变组织中的基因表达（即，数量性状基因座（eQTL）研究）集中于分子数量性状。由于传统的GWAS分析由于其不可知的方法而遭受功率损失，因此需要新的策略来识别胶质瘤风险的额外的和科学上有意义的易感性位点。因此，我们建议整合eQTL研究，以更有力地测试SNP对疾病的影响，在GWAS中，当eQTL研究和GWAS在不同的主题之间进行。通过建立SNPs和基因表达对疾病风险的联合作用的回归模型，我们开发了一种有效的测试程序，用于测试基因或途径中eQTL SNP集的整体效应，并说明了其在数值模拟研究和哮喘研究中的实用性。我们将进行综合分析，有三个具体的研究目标。在目标1中，我们将使用从神经学正常受试者获得的死后脑组织中收集的全基因组SNP和表达数据进行eQTL分析。在目标2中，我们将首先在目标1中鉴定的基因或免疫调节途径中形成eQTL SNP集，然后使用公开可用的胶质瘤风险的GWAS数据进行eQTL SNP集分析以研究eQTL SNP与胶质瘤风险的关联。在目标3中，我们将对目标2中鉴定的基因的SNP集及其表达值进行综合分析，以评估基于基因的对神经胶质瘤风险的影响，无论是通过eQTL SNP的直接影响还是通过基因表达介导的间接影响。本项目的目标是建立一个新的框架进行和分析GWAS和确定新的易感基因座胶质瘤。作为不同的基因组数据（即，如果在分析中整合SNP和基因表达），我们将期望比仅SNP分析更大的统计功效来检测疾病驱动易感性基因座。此外，我们的eQTL整合方法的结果也将比传统的不可知GWAS更具生物学意义和可解释性，因为eQTL SNP更可能是功能性的，并且eQTL对疾病风险的影响被明确建模。