Breast Cancer GWAS: Function and Environmental Interactions

描述(申请人提供)：乳腺癌等常见疾病的易感性是复杂的。在最低限度上，易感性的病因学集中在大量相互作用的遗传因素上，这些遗传因素单独或共同与环境成分相互作用。为了将风险分配给个人，与群体不同，有必要细化个人固有的风险等位基因，以及它们与彼此和个人环境的相互作用。为了实现个体风险评估及其缓解的目标，需要针对疾病的集成遗传系统网络。工作重点将集中在乳腺癌全基因组关联研究(GWAS)上，该SNPs位于人类基因组的约10%，与大鼠高度定义的乳腺易感性QTL同源。将从健康女性的至少50个乳房整形成形术(HMEC)样本中获得高通量基因表达测量以及全基因组SNP基因型。将确定表达数量性状基因座(EQTL)，并将其与GWAS结果相结合，以预测乳腺癌风险。综合数据集将用于三个重要目的。首先，它们将被用来为一组来自乳腺癌GWAs的标记SNP等位基因分配功能。第二个将是建立网络系统模型，提出SNP和下游表型之间的潜在因果关系。这些综合数据集的第三个应用将是使用威斯康星州乳腺癌病例对照DNA样本(n=~7,000)对可疑但尚未验证的Tag SNP风险等位基因进行优先排序，以进行进一步的验证研究。下一步，研究环境因素对HMEC基因表达的影响，将进一步开发并从功能上探索上述转录组/网络。HMEC的原代培养将暴露在利用先验知识选择的外源物质中。将对感兴趣基因的表达水平进行评估，询问这些制剂(毒性和预防性)是否可以调节与GWASNPs相关的重要转录组的表达，以及暴露是否会显着改变网络结构。与特定外源生物引起的基因表达变化相关的GWASSNPs将被用来确定这些SNPs的分层是否改变了威斯康星州病例对照人群中该环境病原体的相对风险。最后，将使用最初用于人类研究的同源大鼠乳腺癌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.