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

项目摘要/摘要 与孟德尔疾病形成鲜明对比的是，大多数复杂的与特征相关的常见变异映射到 非蛋白质编码区。因为有一种不太发达的遗传密码，对于更大的非 基因组的蛋白质编码部分，识别基因(S)和潜在的非因果等位基因 孟德尔式的/复杂的特征是一个挑战。鉴于全基因组关联的速度 研究正在发现与复杂性状、因果等位基因和易感性相关的区域 基因识别已经成为严重的瓶颈。这项提案的总体目标是勾勒出一个 严谨而全面的策略，以发现功能因果变异及其目标基因。而当 该提案聚焦于前列腺癌，该策略可应用于任何非蛋白质编码基因座。 中心假设是癌症风险基因是调控因素。最近的数据令人信服 证明Gwas基因座富含调控元件。监管要素控制着 基因的表达。因果变异很难发现，因为科学界不太熟悉 在注释基因组的非蛋白质编码部分时。这项提议旨在发展一部小说 计算和统计框架，以确定候选因果变量的优先顺序，然后进行实验 验证这些预测。该提案将对数量性状基因座(QTL)和等位基因不平衡进行联合建模 (AI)新框架中表观遗传数据(CHIP-SEQ和ATAC-SEQ)和转录本(RNA-SEQ)中的信号 我们称之为环路广谱联合研究(CWAS)。最重要的Cwas基因座将受到 表观基因组和基因组编辑，以从功能上表征和识别因果变异。 目标1将利用新的实验方法来创建大规模数据集，这些数据集将为目标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... 用于注释活性增强子的吞吐量测序(CHIP-SEQ)，转座酶可访问的分析 染色质(ATAC-seq)用于鉴定开放染色质，RNA-seq.所有这些数据都将受到目标 在一组预定义变量上进行浓缩，这将使人工智能能够进行严格和系统的测量 在杂合子位置。目标2将在结构化框架中利用这些数据来计算识别 统计学上有意义的CWAS前列腺癌风险基因。这些基因座将在AIM 3进行实验测试， 对候选人的聚类规则间隔短回文重复(CRISPR)评估 将执行因果变种。 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， 致病的)导致人类前列腺癌的变异。发现人类潜在的机制 特征不仅会为疾病的生物学提供信息，还可能揭示出更理性的机会 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.