Identifying causal variants and genes underlying breast cancer risk loci

 描述（由申请人提供）：与孟德尔疾病形成鲜明对比，大多数复杂性状相关的常见变异映射到非蛋白质编码区。由于基因组中较大的非蛋白质编码部分的遗​​传密码不太发达，因此识别非孟德尔/复杂性状背后的基因和因果等位基因提出了挑战。鉴于全基因组关联研究（GWAS）快速发现与复杂性状相关的区域，基因和因果等位基因的识别已成为严重的瓶颈。该提案的总体目标是概述一个严格的策略来发现复杂性状背后的功能因果变异和基因。虽然该提案的重点是乳腺癌，但这些策略是通用的，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。最近的数据令人信服地表明，GWAS 基因座的调控元件得到了富集。调控元件控制基因的表达水平。因果基因和变异很难发现，因为科学界不太擅长注释基因组的非蛋白质编码部分。该提案旨在利用表达数量性状位点（eQTL）、环状染色体构象捕获（4C）和基因组编辑这三种强大的工具来识别因果基因和等位基因。目标 1 和目标 2 在逻辑和结构上相似 - 识别增强子-靶基因相互作用（在目标 1 中使用 eQTL，在目标 2 中使用 4C/TALE-LSD1），使用与表观遗传分析相交的病例对照精细作图数据识别候选因果变异，并对候选因果变异进行基因组编辑。影响预定读数的变异——基因表达（目标 1）或等位基因特异性表达（目标 2）的变化——将被视为因果功能多态性。与此同时，将对候选因果变异进行聚类规则间隔短回文重复 (CRISPR) 评估。来自这些测定的信息将与遗传和表观遗传数据相结合，以定义功能因果变异。目标3将测试细胞中的目标基因 基于模型来了解它们对癌症相关表型的影响，例如增殖和侵袭。在该项目完成后，我们完全预计我们将开始解开引发人类乳腺癌的基因/途径。发现前列腺癌的潜在机制不仅可以了解这种疾病的生物学原理，还可以揭示更合理地干预治疗和预防的机会。

 DESCRIPTION (provided by applicant): 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, gene and causal allele identification have become severe bottlenecks. The overall goal of this proposal is to outline a rigorous strategy to discover functionally causal variants and genes underlying complex traits. While the proposal focuses on breast cancer, the strategies are generic and 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 genes and 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 utilize three powerful tools, expression quantitative trait loci (eQTL), circular chromosome conformation capture (4C) and genome editing to identify causal genes and alleles. Both Aims 1 and 2 are logically and structurally similar - identify enhancer-target gene interactions (using eQTL in Aim 1 and 4C/TALE-LSD1 in Aim 2), identify candidate causal variants using case-control fine mapping data intersected with epigenetic profiling, and perform genome editing on candidate causal variants. The variant that affects the predetermined readout - changes in gene expression (Aim 1) or allele- specific expression (Aim 2) - will be deemed a causal functional polymorphism. In parallel, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) evaluation of the candidate causal variants will be performed. Information from these assays will be integrated with genetic and epigenetic data to define the functionally causal variant. Aim 3 will test the target genes in cell based models to understand their influence on cancer-related phenotypes, such as proliferation and invasion. At the completion of this project, we fully anticipate that we will have begun to unravel the genes/pathways that initiate human breast cancer. Discovering the mechanisms underlying prostate cancer will not only inform the biology of this disease, but may also reveal opportunities to more rationally intervene in treatment and prevention.