Identification and validation of cell specific eQTLs by Bayesian modeling

通过贝叶斯模型识别和验证细胞特异性 eQTL

基本信息

批准号：
8586116
负责人：
Christopher David Brown
金额：
$ 40.22万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-08-01 至 2016-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8586116
关键词：
Accounting Address Affect Alleles Basic Science Biological Assay Cancer Etiology Cataloging Catalogs Cells Collection Complex Confounding Factors (Epidemiology)Data Detection Discipline Disease Disease Association Drug Design Drug Targeting Follow-Up Studies Functional RNA Gene Expression Genes Genetic Genomics Genotype Goals Hand Human Individual Joints Linkage Disequilibrium Maps Modeling Nucleotides Outcome Pattern Phenotype Play Protocols documentation Quantitative Trait Loci Regulatory Element Reporter Research Research Proposals Resolution Risk Role Single Nucleotide Polymorphism Somatic Mutation Specific qualifier value Specificity Statistical Models Structure Testing Tissues Translating Translations Validation Variant Work cell type disorder risk genetic variant genome wide association study human disease improved interest knock-down novel prevent promoter public health relevance research study screening success transcription factor

项目摘要

DESCRIPTION (provided by applicant): Project Summary Non-coding single nucleotide polymorhpisms (SNPs) account for over 85% of the genotype-phenotype associations identified in genomewide association studies (GWAS), yet we understand almost nothing about their functional mechanisms. Numerous lines of evidence demonstrate that regulatory SNPs play causal roles in many complex human phenotypes. GWAS associations are enriched for variants associated with gene expression levels (eQTLs) and within cis-regulatory elements (CREs). Because eQTLs and CREs are often functional in a subset of cell types, and because a particular cell type is often of interest for a disease, it is critical that analyses of GWAS-eQTL overlap consider cell specificity. Our long term research objective is to determine, for every non-coding SNP, if it is functional in a particular cell type and, if so, the specific mechanism by which it functions. In order to reach this goal, we need to have in hand a large set of cell specific, causal functional SNPs from which we can begin to generalize; the results from current eQTL studies are typically insufficient because they are not always relevant for a cell type of interest, they identify tag SNPs instead of the causal SNP, and they do not integrate CREs. Our objectives in this proposal are to develop statistical models to identify, quantify, and functionally interpret cell specific eQTLs in cis and trans, and to experimentally validate causal variant predictions using novel massively parallel CRE reporter assays. In Aim 1, we will develop multivariate Bayesian regression models that will improve power for eQTL detection, improve the interpretibility of eQTL cell specificity, and identify the CREs through which each SNP functions. In Aim 2, we will develop structured sparse latent factor models to identify cell specific gene coexpression modules that will be used to identify trans-eQTLs while simulataneously controlling for hidden confounding variables. In Aim 3, we will develop and apply massively parallel CRE reporter assays to validate thousands of predicted causal variants that underlie eQTL associations. With such a large collection of cell specific causal eQTNs and CREs in hand, we hope to mechanistically interpret GWAS associations, identify cancer-causing somatic mutations, and specify novel drug targets for human disease.

描述（由申请人提供）：项目摘要非编码单核苷酸多聚症（SNP）占了超过85％的基因型 - 表型关联，在全基因组关联研究（GWAS）中鉴定出来，但我们几乎对它们的功能机制一无所知。许多证据表明，调节性SNP在许多复杂的人类表型中起因果关系。与基因表达水平（EQTL）和顺式调节元件（CRES）相关的变体，GWAS关联富含。由于EQTL和CRE通常在细胞类型的子集中起作用，并且由于特定的细胞类型通常是疾病感兴趣的，因此对GWAS-EQTL分析重叠的分析考虑细胞特异性至关重要。我们的长期研究目标是为每个非编码SNP确定它在特定的细胞类型中起作用，如果是的，则确定其功能的特定机制。为了实现此目标，我们需要手头有一套特定于细胞的因果功能SNP，我们可以从中开始概括。目前的EQTL研究的结果通常不足，因为它们并不总是与细胞类型相关，而是识别标签SNP而不是因果SNP，并且不整合CRES。我们在本提案中的目标是开发统计模型，以识别，量化和解释CIS和Trans中细胞特异性EQTL，并使用新型平行的CRE报告基因测定法进行实验验证因果变异预测。在AIM 1中，我们将开发多元贝叶斯回归模型，这些模型将提高EQTL检测的功能，提高EQTL细胞特异性的解释性，并确定每个SNP功能的CRE。在AIM 2中，我们将开发结构化的稀疏潜在因子模型，以识别细胞特定基因共表达模块，这些模块将用于识别反式EQTL，同时模拟控制隐藏的混杂变量。在AIM 3中，我们将开发并应用大量并行的CRE报告基因测定法，以验证数千种eqtl关联基础的预测因果变体。掌握了大量细胞特异性因果EQTN和CRE，我们希望能够机械地解释GWAS关联，确定引起癌症的体细胞突变，并指定针对人类疾病的新型药物靶标。