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