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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：
8708217
负责人：
Christopher David Brown
金额：
$ 39.29万
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/8708217
关键词：
Accounting Address Affect Alleles Basic Science Bayesian Modeling 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态性 (SNP) 占全基因组关联研究 (GWAS) 确定的基因型-表型关联的 85% 以上，但我们对其功能机制几乎一无所知。大量证据表明，调节性 SNP 在许多复杂的人类表型中发挥着因果作用。 GWAS 关联丰富了与基因表达水平 (eQTL) 和顺式调控元件 (CRE) 相关的变异。由于 eQTL 和 CRE 通常在细胞类型子集中发挥作用，并且特定细胞类型通常与疾病相关，因此 GWAS-eQTL 重叠分析考虑细胞特异性至关重要。我们的长期研究目标是确定每个非编码 SNP 是否在特定细胞类型中发挥作用，如果是，则确定其发挥作用的具体机制。为了实现这一目标，我们需要掌握大量细胞特异性、因果功能的 SNP，从中我们可以开始进行概括；目前的 eQTL 研究结果通常是不够的，因为它们并不总是与感兴趣的细胞类型相关，它们识别标签 SNP 而不是因果 SNP，并且它们不整合 CRE。我们在本提案中的目标是开发统计模型来识别、量化和功能解释顺式和反式的细胞特异性 eQTL，并使用新型大规模并行 CRE 报告基因检测来实验验证因果变异预测。在目标 1 中，我们将开发多元贝叶斯回归模型，该模型将提高 eQTL 检测的能力，提高 eQTL 细胞特异性的可解释性，并识别每个 SNP 发挥作用的 CRE。在目标 2 中，我们将开发结构化稀疏潜在因子模型来识别细胞特异性基因共表达模块，该模块将用于识别反式 eQTL，同时控制隐藏的混杂变量。在目标 3 中，我们将开发并应用大规模并行 CRE 报告基因检测，以验证构成 eQTL 关联的数千个预测因果变异。有了如此大量的细胞特异性因果 eQTN 和 CRE，我们希望能够从机制上解释 GWAS 关联，识别致癌的体细胞突变，并指定针对人类疾病的新药物靶点。