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

MolQTL：人类癌症分子数量性状位点的综合资源。

• 批准号: 10933833
• 负责人: Leng Han
• 金额: $ 39.63万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-11 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10933833
• 关键词: MolQTL; comprehensive; resource; molecular; quantitative

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.

抽象的 重要的 非编码 功能性的 数据 我们组 构建了一系列molQTL的数据门户，包括表达QTL（eQTL）的数据门户， 基于大量癌症样本的甲基化 QTL (meQTL) 和剪接 QTL (sQTL) TCGA。我们证明这些 QTL 与患者生存相关，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， 核苷酸多态性 (SNP) 是最常见的人类遗传变异类型， 在塑造复杂的人类特征和引起疾病方面发挥着重要作用。大多数与风险相关的 SNP 位于 区域，了解其影响和分子机制仍然是一个挑战 单核苷酸多态性 (SNP)。 ) 分析是一种链接基因分型的统计方法 和分子表型数据来解释遗传变异对复杂性状的影响。 单身的 , 分子数量性状位点（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 molQTL 在精准医学中的应用。 该提案的目标是增强、扩展和推广我们现有的数据资源，这将 通过 molQTL 分析连接遗传变异和不同的分子特征，提供独特的数据 用于了解遗传变异的功能影响并促进获取和利用的资源 为非专家用户理解复杂的数据集。在目标 1 中，我们将增强现有的数据资源 带有附加的分析模块。我们将通过高效、准确的方法识别 molQTL（目标 1.1）。我们将通过中介分析精细绘制因果变异和因果效应（目标 1.2）。我们将 评估 molQTL 的抗癌药物反应（目标 1.3）。我们将确定之间的关联 通过 molQTL 分析了解遗传变异和免疫特征（目标 1.4）。在目标 2 中，我们将扩展并 推广我们现有的数据资源。我们将鉴定RNA编辑QTL（edQTL，目标2.1），3'-UTR替代品 多腺苷酸化 QTL（apaQTL，目标 2.2）和蛋白质 QTL（pQTL，目标 2.3）。我们将开发统一的数据 整合本提案中描述的所有 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、视频教程和实践与用户社区积极互动 研讨会（目标 2.5）。我们期望我们的 molQTL 数据门户将成为一个全面、独特且用户友好的平台。 友好的数据门户，用于识别和解释遗传变异的功能后果。