Elucidating prostate cancer risk mechanisms through large-scale cistrome wide association studies

通过大规模顺反组广泛关联研究阐明前列腺癌风险机制

基本信息

批准号：
10686418
负责人：
MATTHEW L FREEDMAN
金额：
$ 66.05万
依托单位：
DANA-FARBER CANCER INST
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10686418
关键词：
ATAC-seq Acetylation Affect Algorithms Alleles Allelic Imbalance Automobile Driving Benign Binding Sites Biological Assay Biology CRISPR/Cas technology Cell Line ChIP-seq Chromatin Chromosomes Clustered Regularly Interspaced Short Palindromic Repeats Code Communities Complex Data Data Set Disease Elements Enhancers Epigenetic Process Evaluation Gene Expression Gene Expression Profiling Generations Genes Genetic Genetic Code Genetic Transcription Genome Genotype Goals Heritability Heterozygote High-Throughput Nucleotide Sequencing Human Human Genetics Hybrids Individual Libraries Link Location Malignant neoplasm of prostate Maps Measurement Measures Mediating Mendelian disorder Mendelian randomization Methods Modeling Molecular Open Reading Frames Pathogenesis Pathogenicity Population Prevention Prostate Proteins Quantitative Trait Loci RNA Regulatory Element Risk Sampling Signal Transduction Site Specimen Structure Susceptibility Gene Testing Transcript Transposase Untranslated RNA Validation Variant cancer risk candidate identification candidate validation causal variant chromatin immunoprecipitation disorder risk epigenome epigenome editing epigenomics genetic risk factor genetic variant genome editing genome wide association study genome-wide genome-wide analysis histone modification in vivo innovation large scale data novel prostate cancer risk risk variant scale up tool trait transcription factor transcriptome sequencing transcriptomics tumor

项目摘要

PROJECT SUMMARY/ABSTRACT In stark contrast to Mendelian disorders, the majority of complex trait-associated common variants map to non-protein coding regions. Since there is a less well-developed genetic code for the much larger non- protein coding portion of the genome, identifying the gene(s) and causal alleles underlying non- Mendelian/complex traits presents a challenge. Given the rapidity with which genome wide association studies (GWAS) are discovering regions associated with complex traits, causal allele and susceptibility gene identification have become severe bottlenecks. The overall goal of this proposal is to outline a rigorous and comprehensive strategy to discover functionally causal variants and their target genes. While the proposal focuses on prostate cancer, the strategies can be applied to any non-protein coding locus. The central hypothesis is that cancer risk loci are regulatory elements. Recent data convincingly demonstrate that GWAS loci are enriched for regulatory elements. Regulatory elements control the level of expression of genes. Causal variants are difficult to discover because the scientific community is less adept at annotating the non-protein coding portion of the genome. This proposal seeks to develop a novel computational and statistical framework to prioritize candidate causal variants and then to experimentally validate these predictions. The proposal will jointly model quantitative trait loci (QTL) and allelic imbalance (AI) signals in epigenetic data (ChIP-seq and ATAC-seq) and transcripts (RNA-seq) in a novel framework that we term cistrome wide association studies (CWAS). The most significant CWAS loci will be subjected to epigenome and genome editing to functionally characterize and identify causal variants. Aim 1 will utilize novel experimental methods to create the large-scale datasets that will inform Aim 2. The ultimate goal of Aim 1 is to perform H3K27 acetylation, and AR chromatin immunoprecipitation and high- throughput sequencing (ChIP-seq) to annotate active enhancers, Assay for Transposase-Accessible Chromatin (ATAC-seq) to identify open chromatin, and RNA-seq. All of these data will be subjected to target enrichment at a predefined set of variants, which will enable the rigorous and systematic measurement of AI at heterozygote sites. Aim 2 will utilize these data in a structured framework to computationally identify statistically significant CWAS prostate risk loci that. These loci will be experimentally tested in Aim 3 where Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) evaluation of the candidate causal variants will be performed. At the completion of this project, we fully anticipate that we will have begun to unravel the causal (i.e., pathogenic) variants that initiate human prostate cancer. Discovering the mechanisms underlying human traits will not only inform the biology of disease, but may also reveal opportunities to more rationally intervene in treatment and prevention.

项目概要/摘要与孟德尔疾病形成鲜明对比的是，大多数与性状相关的复杂常见变异映射到非蛋白质编码区。由于更大的非生物体的遗传密码不太发达基因组的蛋白质编码部分，识别非非相关基因和因果等位基因孟德尔/复杂特征提出了挑战。鉴于基因组广泛关联的速度研究 (GWAS) 正在发现与复杂性状、因果等位基因和易感性相关的区域基因鉴定已成为严重的瓶颈。该提案的总体目标是概述严格而全面的策略来发现功能性因果变异及其目标基因。尽管该提案的重点是前列腺癌，该策略可以应用于任何非蛋白质编码位点。中心假设是癌症风险位点是调控元件。最新数据令人信服证明 GWAS 位点富含调控元件。监管要素控制水平基因的表达。因果变异很难发现，因为科学界不太擅长注释基因组的非蛋白质编码部分。该提案旨在开发一种新颖的计算和统计框架，以优先考虑候选因果变异，然后进行实验验证这些预测。该提案将联合模拟数量性状位点（QTL）和等位基因失衡新框架中的表观遗传数据（ChIP-seq 和 ATAC-seq）和转录本（RNA-seq）中的 (AI) 信号我们称之为顺式异构体广泛关联研究（CWAS）。最重要的 CWAS 基因座将受到影响表观基因组和基因组编辑以功能表征和识别因果变异。目标 1 将利用新颖的实验方法创建大规模数据集，为目标 2 提供信息。目标 1 的最终目标是进行 H3K27 乙酰化、AR 染色质免疫沉淀和高通量测序 (ChIP-seq) 注释活性增强子、转座酶可及性检测染色质 (ATAC-seq) 用于识别开放染色质，以及 RNA-seq。所有这些数据都将受到目标丰富一组预定义的变体，这将使人工智能能够进行严格和系统的测量在杂合子位点。目标 2 将在结构化框架中利用这些数据来计算识别具有统计学显着性的 CWAS 前列腺风险位点。这些位点将在目标 3 中进行实验测试，其中对候选者的聚类规则间隔短回文重复 (CRISPR) 评估将执行因果变体。在这个项目完成时，我们完全预计我们将开始揭开因果关系（即，致病）变异，引发人类前列腺癌。发现人类的潜在机制特征不仅可以为疾病的生物学提供信息，还可以揭示更理性地认识疾病的机会干预治疗和预防。