Breast Cancer GWAS: Function and Environmental Interactions

描述（由申请人提供）：乳腺癌等常见疾病的易感性是复杂的。最小程度上，易感性的病因集中在大量相互作用的遗传因素上，这些遗传因素单独或集体地与环境因素相互作用。为了将风险分配给个人，而不是群体，有必要细化个人固有的风险等位基因及其相互作用和个人的环境。为了实现个体风险评估和降低风险的目标，需要针对特定疾病的综合遗传系统网络。工作将集中在乳腺癌全基因组关联研究（GWAS）中约10%的人类基因组中与大鼠高度定义的乳腺易感性qtl同源的snp。将获得来自健康女性的至少50个缩小乳房成形术人乳腺上皮细胞（HMEC）样本的高通量基因表达测量以及全基因组SNP基因型。表达数量性状位点（eQTL）将被确定并与GWAS结果相结合用于乳腺癌风险。综合数据集将用于三个重要目的。首先，它们将用于为一组来自乳腺癌GWAS的标签SNP等位基因分配功能。第二个将是建立网络系统模型，提示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个应用将是优先考虑可疑但尚未验证的标签SNP风险等位基因，以便使用威斯康星州乳腺癌病例对照DNA样本（n = ~ 7000）进行进一步的验证研究。接下来，研究环境因素对HMEC基因表达的影响将进一步开发和功能探索上述转录本的组/网络。HMEC的原代培养将暴露于使用先前知识选择的外源物中。将评估感兴趣的基因的表达水平，询问这些药物（毒性和预防性）是否可以调节与GWAS snp相关的重要转录本组的表达，以及暴露是否显着改变网络结构。GWAS snp与特定外源药物引起的基因表达变化相关，将用于确定这些snp的分层是否会改变威斯康星州病例对照人群中该环境因子的相对风险。最后，将使用最初用于集中人类研究的遗传大鼠乳腺癌发生模型进行体内验证研究。

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.