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（由申请人提供）：对乳腺癌等常见疾病的敏感性很复杂。易感性的病因最少以大量相互作用的遗传元素为中心，这些遗传元素与环境成分单独和集体相互作用。为了向个人分配风险，与人口相比，有必要完善个人固有的风险等位基因及其彼此及其环境的互动。为了实现个人风险估计的目标及其缓解，需要特定于疾病的综合遗传系统网络。工作将重点放在乳腺癌全基因组关联研究（GWAS）SNP中，其中约10％的人类基因组与大鼠中高度定义的乳明易感性QTL同源。将获得来自至少50种还原乳腺成形术的人类乳腺上皮细胞（HMEC）样品的高吞吐量基因表达测量，并将获得来自健康女性的全基因组SNP基因型。将鉴定出表达定量性状基因座（EQTL），并与GWAS的乳腺癌风险结果融合。集成数据集将用于三个重要目的。首先，它们将用于将功能分配给乳腺癌GWAS的一组TAG SNP等位基因。第二个是建立网络系统模型，以暗示SNP和下游表型之间潜在的因果关系。这些集成数据集的第三次应用将是使用威斯康星州乳腺癌病例对照DNA样本（n = 〜7,000）确定可疑但尚未验证的TAG SNP风险等位基因进行进一步验证研究。接下来，研究环境因素对HMEC基因表达的影响将进一步发展，并在功能上探索上述转录本的组/网络。 HMEC的主要培养物将暴露于使用先验知识选择的异生元。将评估感兴趣的基因的表达水平，询问此类药物（有毒和预防性）是否可以调节与GWAS SNP相关的重要转录本的表达，以及暴露是否会显着改变网络结构。与特定异种生物学引起的基因表达变化相关的GWAS SNP将用于确定这些SNP分层是否会改变威斯康星州病例对照人群中该环境药物的相对风险。最后，将使用最初用于重点人类研究的先天大鼠乳腺癌发生模型进行体内验证研究。 公共卫生相关性：该项目的目的是开发一种结合全球遗传信息以及环境暴露的综合方法，以形成一种开始描述乳腺癌病因的网络模型。这样的模型在完成后可以使我们从对乳腺癌的人口风险的估计转变为个人风险。该模型还将提供基本的遗传/环境风险的功能信息，这可能导致降低这种疾病风险的策略。