MolQTL: A comprehensive resource for molecular quantitative trait loci in human cancer.

摘要 重要信息 非编码 功能 给我们的小组提供数据 构建了一系列MolQTL数据门户，包括表达QTL数据门户(EQTL)， 甲基化QTL(MeQTL)和剪接QTL(SQTL) TCGA。我们证明了这些QTL与患者的存活率相关，和/或与GWAS重叠 连锁不平衡区域。这些相关数据资源自发布以来已被广泛访问， 核苷酸多态(SNPs)是人类最常见的遗传变异类型， 在塑造复杂的人类特征和引发疾病方面的作用。大多数与风险相关的SNPs位于 因此，要了解其影响和分子机制仍然是一个挑战。 SNPs。)分析是一种将基因分型联系起来的统计方法 和分子表型数据来解释遗传变异对复杂性状的影响。 单人 ， 分子数量性状座位(MolQTL 并突出显示 发现 有机会了解遗传变异的功能意义并利用 分子QTL在精确医学中的应用。 该提案的目标是增强、扩展和提升我们现有的数据资源，这些资源将 通过分子QTL分析将遗传变异和不同的分子特征联系起来，提供了独特的数据 了解遗传变异的功能影响并促进获取和 为非专家用户理解复杂的数据集。在目标1中，我们将增强我们现有的数据资源 带有额外的分析模块。我们将用高效和准确的方法(AIM)识别分子QTL 1.1)。我们将通过中介分析精细绘制因果变量和因果影响图(目标1.2)。我们会 从分子QTL评价抗癌药物的反应(目标1.3)。我们将确定两者之间的关联 通过分子QTL分析遗传变异和免疫特征(目标1.4)。在目标2中，我们将扩大和 提升我们现有的数据资源。我们将确定RNA编辑QTL(edQTL，Aim 2.1)，3‘-UTR替代 多聚腺苷化QTL(apaQTL，Aim 2.2)和蛋白质QTL(pQTL，Aim 2.3)。我们将开发一个统一的数据 整合本建议书中描述的所有molQTL类型的门户(目标2.4)。我们将推广MolQTL和 通过提供书面文档、视频教程和动手操作，与用户社区积极互动 讲习班(目标2.5)。我们期望我们的MolQTL数据门户将作为一个全面、独特和用户- 友好的数据门户，用于识别和解释基因变异的功能后果。

Abstract important noncoding functional data Our group constructed a series of data portals for molQTLs, including data portals for expression QTLs (eQTLs), methylation QTLs (meQTLs), and splicing QTLs (sQTLs) based on a large number of cancer samples from TCGA. We demonstrated that these QTLs are associated with patient survival, and/or overlap with GWAS linkage disequilibrium regions. These related data resources have been broadly accessed since their releases, nucleotide polymorphisms (SNPs), the most common type of human genetic variants, play roles in shaping complex human traits and causing diseases. Most risk-related SNPs are located in regions and it remains a challenge to understand the effects and molecular mechanisms of SNPs . ) analysis is a statistical method to link genotyping and molecular phenotype data to interpret the effects of genetic variants in complex traits. Single , Molecular quantitative trait loci (MolQTL and highlighted discovery the opportunities t o understand the functional significance of genetic variants and to utilize the of molQTLs in precision medicine. The goal of this proposal is to enhance, expand, and promote our existing data resources that will bridge the genetic variants and different molecular features through molQTL analysis, providing a unique data resource for understanding the functional effects of genetic variants and facilitating access to and understanding of complex datasets for non-expert users. In Aim 1, we will enhance our existing data resources with additional analytical modules. We will identify molQTLs with highly efficient and accurate approaches (Aim 1.1). We will fine-map causal variants and causal effects through mediation analysis (Aim 1.2). We will evaluate anti-cancer drug response from molQTLs (Aim 1.3). We will determine the associations between genetic variants and immune features through molQTL analysis (Aim 1.4). In Aim 2, We will expand and promote our existing data resources. We will identifyRNA editing QTLs (edQTLs, Aim 2.1), 3'-UTR alternative polyadenylationQTLs (apaQTLs, Aim 2.2), andprotein QTLs (pQTLs, Aim 2.3). We will develop a unified data portal to integrate all the molQTL types described in this proposal (Aim 2.4). We will promote MolQTL and active interaction with the user community through providing written documents, video tutorial and hands-on workshops (Aim 2.5). We expect that our molQTL data portal will serve as a comprehensive, unique, and user- friendly data portal to identify and interpret the functional consequences of genetic variants.