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

项目总结/摘要 与孟德尔疾病形成鲜明对比的是，大多数复杂的性状相关的常见变异都映射到 非蛋白质编码区。由于有一个不太发达的遗传密码，为更大的非- 基因组的蛋白质编码部分，识别基因和导致非- 孟德尔/复杂性状提出了挑战。考虑到基因组范围内的关联 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 基因鉴定已经成为严重的瓶颈。本提案的总体目标是概述一个 严格和全面的战略，以发现功能上的因果变异及其靶基因。而 该建议侧重于前列腺癌，该策略可应用于任何非蛋白质编码位点。 核心假设是癌症风险基因座是调节元件。最近的数据令人信服 证明GWAS基因座富含调节元件。监管要素控制着 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。这一提议旨在发展一部小说， 计算和统计框架，以优先考虑候选因果变异，然后通过实验 验证这些预测。该提案将联合建模数量性状基因座（QTL）和等位基因不平衡 (AI)新框架中的表观遗传数据（ChIP-seq和ATAC-seq）和转录本（RNA-seq）中的信号 我们称之为顺式全关联研究（cistrome wide association studies，CWAS）。最重要的CWAS基因座将被 表观基因组和基因组编辑，以功能性地表征和鉴定致病变体。 目标1将利用新的实验方法来创建大规模数据集，为目标2提供信息。的 目的1的最终目标是进行H3 K27乙酰化，AR染色质免疫沉淀和高- 通量测序（ChIP-seq）来注释活性增强子，转座酶可重复性测定 染色质（ATAC-seq）以鉴定开放染色质，和RNA-seq。所有这些数据都将受到目标 在预定义的一组变体上进行富集，这将使人工智能的严格和系统的测量成为可能。 在杂合子位点。目标2将在结构化框架中利用这些数据， 具有统计学意义的CWAS前列腺风险位点。这些位点将在目标3中进行实验测试， CRISPR（Regularly Interspaced Short Palindromic Repeats） 将执行因果变量。 在这个项目完成时，我们完全预计我们将开始解开因果关系（即， 致病性）变体，其引发人前列腺癌。发现人类的潜在机制 性状不仅会告知疾病的生物学，而且还可能揭示更合理地 干预治疗和预防。

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.