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: 9083278
• 负责人: MATTHEW L FREEDMAN
• 金额: $ 61.62万
• 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 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9083278
• 关键词: 8q24; Affect; Alleles; Allelic Imbalance; Amino Acid Sequence; Area; Biological Assay; Biology; Breast; 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; Gene Targeting; Generic Drugs; Genes; Genetic; Genetic Code; Genetic Polymorphism; Genome; Genotype; Genotype-Tissue Expression Project; Goals; Human; Human Genetics; Lead; Linkage Disequilibrium; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Mendelian disorder; Methods; Modeling; Pathway interactions; Phenotype; Prevention; Proteins; Proto-Oncogenes; Publications; Quantitative Trait Loci; Regulatory Element; Technology; Testing; Transcript; Untranslated RNA; Variant; base; cancer risk; case control; chromosome conformation capture; epigenetic profiling; epigenome; gene interaction; genetic risk factor; genetic variant; genome editing; genome wide association study; 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，大多数复杂的与特征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，识别该基因(S)和潜在的非孟德尔/复杂性状的因果等位基因是一个挑战。鉴于全基因组关联研究(GWAS)发现与复杂性状相关的区域的速度之快，基因和因果等位基因识别已成为严重的瓶颈。这项提案的总体目标是勾勒出一项严格的战略，以发现复杂性状的功能因果变异和基因。虽然该提案关注的是乳腺癌，但这些策略是通用的，可以应用于任何非蛋白质编码基因。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地证明，Gwas基因座富含调控元件。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对基因组的非蛋白质编码部分进行注释。这一建议寻求利用三个强大的工具，表达数量性状基因座(EQTL)，环形染色体构象捕获(4C)和基因组编辑来识别因果基因和等位基因。目标1和目标2在逻辑和结构上都是相似的--识别增强子-靶基因的相互作用(在目标1中使用eQTL，在目标2中使用4C/TALL-LSD1)，使用病例对照精细定位数据与表观遗传图谱交叉来识别候选因果变异，并对候选因果变异进行基因组编辑。影响预定读数的变异--基因表达的改变(目标1)或等位基因特异性表达(目标2)--将被认为是因果功能多态。同时，将对候选因果变异进行成簇的规则间隔短回文重复(CRISPR)评估。来自这些检测的信息将与遗传和表观遗传学数据相结合，以确定功能上的因果变异。目标3将在细胞中测试目标基因 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。在这个项目完成时，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。前列腺癌潜在机制的发现不仅将为这种疾病的生物学研究提供信息，还可能揭示更合理地干预治疗和预防的机会。