Identifying causal variants and genes underlying breast cancer risk loci

识别乳腺癌风险位点的因果变异和基因

• 批准号: 9904556
• 负责人: MATTHEW L FREEDMAN
• 金额: $ 59.65万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9904556
• 关键词: 8q24; Affect; Alleles; Allelic Imbalance; Amino Acid Sequence; Area; Biological Assay; Biology; Breast; Breast Cancer Risk Factor; Candidate Disease Gene; Cell Line; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Communities; Complex; DNA; Data; Disease; Enhancers; Epigenetic Process; Etiology; Evaluation; Foundations; Gene Expression; Genes; Genetic; Genetic Code; Genetic Polymorphism; Genome; Genotype; Genotype-Tissue Expression Project; Goals; Human; Human Genetics; KDM1A gene; Linkage Disequilibrium; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Mendelian disorder; Methods; Modeling; Pathway interactions; Phenotype; Prevention; Proteins; Publications; Quantitative Trait Loci; Regulatory Element; Structure; Technology; Testing; Transcript; Untranslated RNA; Variant; base; c-myc Proto-Oncogenes; cancer risk; case control; causal variant; chromosome conformation capture; epigenetic profiling; epigenome editing; gene interaction; genetic risk factor; genetic variant; genome editing; genome wide association study; genomic locus; innovation; insight; malignant breast neoplasm; public health relevance; risk variant; tool; trait

 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.

 描述（由申请人提供）：与孟德尔疾病形成鲜明对比的是，大多数复杂性状相关的常见变体映射到非蛋白质编码区。由于对于基因组的大得多的非蛋白质编码部分存在发育不太完善的遗传密码，因此鉴定非孟德尔/复杂性状的基因和致病等位基因存在挑战。鉴于全基因组关联研究（GWAS）发现与复杂性状相关的区域的快速性，基因和致病等位基因鉴定已成为严重的瓶颈。该提案的总体目标是概述一个严格的策略，以发现功能性因果变异和复杂性状的基因。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。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地表明，GWAS基因座富集的调控元件。调控元件控制基因的表达水平。致病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。该提案旨在利用三个强大的工具，表达数量性状基因座（eQTL），环状染色体构象捕获（4C）和基因组编辑来识别致病基因和等位基因。目的1和2在逻辑上和结构上都是相似的-鉴定增强子-靶基因相互作用（在目的1中使用eQTL，在目的2中使用4C/TALE-LSD 1），使用病例对照精细作图数据和表观遗传分析鉴定候选因果变体，并对候选因果变体进行基因组编辑。影响预定读数的变体-基因表达（Aim 1）或等位基因特异性表达（Aim 2）的变化-将被认为是因果功能多态性。与此同时，将对候选因果变异体进行重复的规则间隔短回文重复序列（CRISPR）评价。这些检测的信息将与遗传和表观遗传数据相结合，以确定功能性因果变异。目的3将在细胞中检测靶基因 基于模型，以了解它们对癌症相关表型的影响，如增殖和侵袭。在这个项目完成后，我们完全预期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为这种疾病的生物学提供信息，还可以揭示更合理地干预治疗和预防的机会。