Identification and validation of cell specific eQTLs by Bayesian modeling

描述(由申请人提供)： 项目概述非编码单核苷酸多态(SNPs)占全基因组关联研究中发现的基因-表型关联的85%以上，但我们对其作用机制几乎一无所知。大量证据表明，调节性SNP在许多复杂的人类表型中起着因果作用。与基因表达水平(EQTL)和顺式调控元件(CRE)相关的变异体丰富了GWAs关联。由于eQTL和CRES通常在细胞类型的子集中起作用，并且特定的细胞类型通常与疾病相关，因此对GWAs-eQTL重叠的分析考虑细胞特异性是至关重要的。我们的长期研究目标是，对于每个非编码的SNP，确定它是否在特定的细胞类型中起作用，如果是，它发挥作用的具体机制。为了达到这一目标，我们需要有大量的细胞特异的、因果的功能SNP，我们可以从这些SNP开始推广；目前的eQTL研究的结果通常是不够的，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，并且它们没有整合CRE。我们在这项建议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中的细胞特异性eQTL，并使用新颖的大规模平行CRE报告分析来实验验证因果变异预测。在目标1中，我们将建立多元贝叶斯回归模型，以提高eQTL的检测能力，提高eQTL细胞特异性的可解释性，并确定每个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，这些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，我们将开发和应用大规模平行的CRE报告分析来验证数千个预测的因果变量，这些变量是eQTL关联的基础。有了如此大量的细胞特异性eQTN和CRE，我们希望从机械上解释GWA的关联，识别导致癌症的体细胞突变，并指定治疗人类疾病的新药物靶点。

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.