Identification and validation of cell specific eQTLs by Bayesian modeling

描述（由申请人提供）： 非编码单核苷酸多态性（SNPs）占全基因组关联研究（GWAS）中确定的基因型-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因果作用。GWAS关联富集与基因表达水平（eQTL）和顺式调控元件（克雷斯）内相关的变体。由于eQTL和克雷斯通常在细胞类型的子集中起作用，并且由于特定细胞类型通常与疾病有关，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在特定细胞类型中是否有功能，如果有，则确定其发挥功能的具体机制。为了达到这一目标，我们需要掌握大量的细胞特异性、因果功能性SNP，我们可以从这些SNP开始进行概括;当前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，它们鉴定标签SNP而不是因果SNP，并且它们不整合克雷斯。我们在这个建议的目标是开发统计模型，以确定，量化，并在功能上解释细胞特异性eQTL的顺式和反式，并通过实验验证因果变异预测使用新的大规模并行CRE报告基因检测。在目标1中，我们将开发多变量贝叶斯回归模型，以提高eQTL检测的能力，提高eQTL细胞特异性的可解释性，并确定每个SNP发挥作用的克雷斯。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trans-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并行CRE报告基因检测，以验证数千个预测的因果变异，这些变异是eQTL关联的基础。有了这样大量的细胞特异性因果eQTN和克雷斯，我们希望从机制上解释GWAS的关联，识别致癌的体细胞突变，并为人类疾病指定新的药物靶点。

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.