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

MolQTL：人类癌症分子数量性状位点的综合资源。

• 批准号: 10593169
• 负责人: Leng Han
• 金额: --
• 依托单位: TEXAS A&M UNIVERSITY HEALTH SCIENCE CTR
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10593169
• 关键词: Antineoplastic Agents; Arthritis; CRISPR/Cas technology; Categories; Clinical; Clinical Trials; Communities; Complex; Data; Data Set; Deposition; Disease; Educational workshop; Epigenetic Process; Event; Genetic Polymorphism; Genomics; Genotype; Goals; Heart Diseases; Human; Human Genetics; Immune; Individual; Investigation; Link; Linkage Disequilibrium; Malignant Neoplasms; Maps; Mediation; Methylation; Molecular; Multiomic Data; Nucleotides; Patients; Pharmaceutical Preparations; Play; Poly A; Polyadenylation; Post-Transcriptional Regulation; Proteins; Proteomics; Quantitative Trait Loci; RNA Editing; RNA Splicing; Research; Research Personnel; Resources; Risk; Role; Sampling; Series; Shapes; Single Nucleotide Polymorphism; Statistical Methods; Technology; The Cancer Genome Atlas; Translating; Untranslated RNA; Untranslated Regions; Update; Variant; Writing; biobank; cancer type; causal variant; complex data; data portal; data resource; database of Genotypes and Phenotypes; disorder risk; genetic architecture; genetic variant; genome editing; genome wide association study; high throughput technology; metabolomics; microbiome; molecular phenotype; multidimensional data; novel; novel diagnostics; novel therapeutic intervention; online resource; phenotypic data; precision medicine; prognostic; response; therapeutic target; trait; translational medicine; translational potential; user-friendly

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 数据我们小组 为MOLQTL构建了一系列数据门户，包括表达QTLS（EQTL）的数据门户， 甲基化QTL（MEQTL）和剪接QTL（SQTL）基于大量癌症样本 TCGA。我们证明了这些QTL与患者的生存有关，并且/或与GWAS重叠 连锁不平衡区域。这些相关的数据资源已被广泛访问，因为它们发行了， 核苷酸多态性（SNP）是人类遗传变异的最常见类型 在塑造复杂的人类特征和引起疾病的角色中的作用。大多数与风险有关的SNP位于 区域和理解的效果和分子机制仍然是一个挑战 SNP。 ）分析是链接基因分型的统计方法 和分子表型数据以解释复杂性状中遗传变异的影响。 单身的 ，，，， 分子定量性状基因座（molqtl 并突出显示 发现 机会了解遗传变异的功能意义，并利用 精密医学中的molqtl。 该提案的目的是增强，扩展和推广我们现有的数据资源 通过MOLQTL分析桥接遗传变异和不同的分子特征，提供了独特的数据 了解遗传变异的功能效应的资源，并促进进入和访问 了解非专家用户的复杂数据集。在AIM 1中，我们将增强现有数据资源 带有其他分析模块。我们将以高效和准确的方法来识别Molqtls（AIM 1.1）。我们将通过调解分析（AIM 1.2）来细微的因果变异和因果关系。我们将 评估MOLQTL的抗癌药物反应（AIM 1.3）。我们将确定 通过MOLQTL分析（AIM 1.4），遗传变异和免疫特征。在AIM 2中，我们将扩展和 促进我们现有的数据资源。我们将识别RNA编辑QTL（EDQTL，AIM 2.1），3'-UTR替代方案 polyadeenylationQTLS（APAQTL，AIM 2.2）和蛋白质QTL（PQTLS，AIM 2.3）。我们将开发统一数据 门户以整合本提案中描述的所有MOLQTL类型（AIM 2.4）。我们将促进molqtl和 通过提供书面文档，视频教程和动手实践，与用户社区的积极互动 研讨会（AIM 2.5）。我们预计我们的Molqtl数据门户将成为一种全面，独特和用户 - 友好的数据门户以识别和解释遗传变异的功能后果。