Breast Cancer GWAS: Function and Environmental Interactions

乳腺癌 GWAS：功能与环境相互作用

• 批准号: 7985105
• 负责人: MICHAEL N GOULD
• 金额: $ 41.38万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-12-11 至 2013-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7985105
• 关键词: Alleles; Architecture; Biological Models; Breast; Breast Cancer Model; Breast Diseases; Case-Control Studies; Chemicals; Collection; Complex; Computational algorithm; DNA; Data; Data Set; Databases; Development; Disease; Environment; Environmental Exposure; Environmental Risk Factor; Epidemiology; Epithelial Cells; Etiology; Exposure to; Gene Expression; Genes; Genetic; Genome; Genotype; Goals; Homologous Gene; Human; Human Genome; Individual; Knowledge; Lead; Mammary Tumorigenesis; Mammary gland; Measurement; Measures; Medicine; Modeling; Pathway interactions; Pharmaceutical Preparations; Phenotype; Population; Predisposition; Prevention strategy; Preventive; Quantitative Trait Loci; Rattus; Relative Risks; Review Literature; Risk; Risk Estimate; Risk Reduction; Sampling; Series; Single Nucleotide Polymorphism; Stratification; Structure; System; Testing; Time; Transcript; Wisconsin; Woman; Work; Xenobiotics; cancer genome; cancer risk; case control; comparative; congenic; disorder prevention; environmental agent; epidemiologic data; genetic element; genome wide association study; high risk; in vivo; interest; malignant breast neoplasm; network models; public health relevance; validation studies

DESCRIPTION (provided by applicant): Susceptibility to common diseases such as breast cancer is complex. Minimally the etiology of susceptibility is centered on a large number of interacting genetic elements which individually and collectively interact with environmental components. In order to assign risks to individuals, in contrast to populations, it will be necessary to refine an individual's inherent risk alleles and their interaction with each other and the individual's environment. To accomplish the goals of individual risk estimation and its mitigation, disease-specific integrated genetic systems networks are needed. Work will focus on breast cancer genome-wide association studies (GWAS) SNPs in ~10% of the human genome that is homologous to the highly defined mammary susceptibility QTLs in the rat. High throughput gene expression measurements from at least 50 reduction mammoplasty human mammary epithelial cells (HMEC) samples from healthy women together with full genome SNP genotypes will be obtained. Expression quantitative trait loci (eQTL) will be identified and integrated with GWAS results for breast cancer risk. The integrated data sets will be used for three important purposes. First, they will be used to assign function to a group of tag SNP alleles from breast cancer GWAS. The second will be to establish network systems models that suggest potential causal relationships among SNPs and downstream phenotypes. The third application of these integrated data sets will be to prioritize suspected but not yet validated tag SNP risk alleles for further validation studies using Wisconsin breast cancer case-control DNA samples (n = ~7,000). Next, investigating the effects of environmental factors on gene expression in HMEC will further develop and functionally explore the groups/networks of transcripts identified above. Primary cultures of HMEC will be exposed to xenobiotics chosen using prior knowledge. The expression levels of genes of interest will be evaluated asking if such agents (toxic and preventive) can modulate the expression of important groups of transcripts associated with GWAS SNPs and if exposure significantly alters network structure. GWAS SNPs that are associated with gene expression changes caused by specific xenobiotics will be used to determine if stratification by these SNPs modifies relative risk for that environmental agent in the Wisconsin case-control population. Finally, in vivo validation studies using the congenic rat mammary carcinogenesis models initially used to focus human studies will be conducted. PUBLIC HEALTH RELEVANCE: The goal of this project is to develop an integrated approach combining global genetic information together with environmental exposure to form a network model that begins to describe the etiology of breast cancer. Such a model, when complete, could allow us to move from the estimation of population risk for breast cancer to individual risk. This model will also provide functional information underlying genetic/environmental risk that could lead to strategies for risk reduction to this disease.

描述（申请人提供）：对乳腺癌等常见疾病的易感性很复杂。最低限度的易感性的病因是集中在大量的相互作用的遗传因素，单独和集体与环境成分相互作用。为了将风险分配给个体，而不是群体，有必要细化个体的固有风险等位基因及其相互作用和个体环境。为了实现个体风险评估和减轻风险的目标，需要疾病特异性集成遗传系统网络。工作将集中在乳腺癌全基因组关联研究（GWAS）SNP中，约10%的人类基因组与大鼠中高度定义的乳腺易感性QTL同源。将获得来自健康女性的至少50个乳房缩小术人乳腺上皮细胞（HMEC）样本的高通量基因表达测量结果以及全基因组SNP基因型。将鉴定表达数量性状基因座（eQTL），并将其与乳腺癌风险的GWAS结果整合。综合数据集将用于三个重要目的。首先，它们将用于分配来自乳腺癌GWAS的一组标签SNP等位基因的功能。第二个将是建立网络系统模型，表明SNPs和下游表型之间的潜在因果关系。这些整合数据集的第三个应用是优先考虑可疑但尚未验证的标签SNP风险等位基因，用于使用威斯康星州乳腺癌病例对照DNA样本（n = ~ 7，000）进行进一步验证研究。接下来，研究环境因素对HMEC中基因表达的影响将进一步开发和功能性探索上述转录物的组/网络。HMEC原代培养物将暴露于使用先前知识选择的外源性物质。将评价感兴趣基因的表达水平，询问这些药物（毒性和预防性）是否可以调节与GWAS SNP相关的重要转录物组的表达，以及暴露是否显著改变网络结构。与特定外源性物质引起的基因表达变化相关的GWAS SNP将用于确定这些SNP的分层是否改变了威斯康星州病例对照人群中该环境因子的相对风险。最后，将使用最初用于关注人类研究的同类大鼠乳腺癌发生模型进行体内验证研究。 公共卫生相关性：该项目的目标是开发一种综合方法，将全球遗传信息与环境暴露结合起来，形成一个网络模型，开始描述乳腺癌的病因。这样一个模型，当完成时，可以让我们从估计乳腺癌的人群风险转移到个人风险。该模型还将提供潜在遗传/环境风险的功能信息，这些信息可能导致降低这种疾病风险的策略。