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.

摘要 重要 非编码 功能 数据我们的团队 构建了一系列molQTL的数据门户，包括表达QTL（eQTL）的数据门户， 甲基化QTL（meQTL）和剪接QTL（sQTL），基于来自 TCGA。我们证明了这些QTL与患者生存率相关，和/或与GWAS重叠。 连锁不平衡区这些相关的数据资源自发布以来已被广泛使用， 核苷酸多态性（SNPs），最常见的人类遗传变异类型， 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SNP位于 区域和它仍然是一个挑战，以了解的影响和分子机制， SNPs。）分析是一种统计学方法， 和分子表型数据来解释复杂性状中遗传变异的影响。 单个 , 分子数量性状基因座 并强调 发现 了解遗传变异的功能意义并利用 molQTLs在精准医学中的应用 本提案的目标是增强、扩展和推广我们现有的数据资源， 通过molQTL分析将遗传变异和不同的分子特征联系起来， 了解遗传变异的功能影响并促进获得和 非专业用户对复杂数据集的理解。在目标1中，我们会加强现有的数据资源， 附加分析模块。我们将用高效和准确的方法鉴定molQTL（Aim 1.1）。我们将通过中介分析（目标1.2）精细映射因果变量和因果效应。我们将 从molQTL评价抗癌药物反应（目标1.3）。我们将确定之间的关联 通过molQTL分析的遗传变异和免疫特征（目的1.4）。在目标2中，我们将扩大和 利用我们现有的数据资源。我们将鉴定RNA编辑QTL（edQTLs，Aim 2.1）、3 '-UTR替代 多聚腺苷酸化QTL（apaQTLs，Aim2.2）和蛋白质QTL（pQTLs，Aim2.3）。我们将制定统一的数据 门户整合本提案中描述的所有molQTL类型（目标2.4）。我们将推广MolQTL， 通过提供书面文件、视频教程和实践，与用户社区积极互动 讲习班（目标2.5）。我们希望我们的molQTL数据门户网站将作为一个全面的，独特的，和用户- 这是一个友好的数据门户，用于识别和解释遗传变异的功能后果。