Whole Genome Association Study of Mammographic Density

描述（由申请人提供）：乳腺摄影密度是乳腺癌最强的风险因素之一。乳房X线摄影密度最高的妇女患乳腺癌的风险是密度最低的妇女的四到六倍。最近，对于其他慢性疾病（例如，冠状动脉疾病），我们已经看到了利用可靠测量的、可遗传的数量性状（例如，循环脂质）是结果的一个强风险因素，可以识别通过结果的全基因组关联研究（GWAS）未识别出的疾病的新基因座。因此，对遗传表型的研究可以揭示生物学途径，从而更好地理解疾病的基本机制，并可能确定干预目标。在类似的范例中，乳房X线摄影密度是一种高度可遗传的、可靠测量的定量特征，并且是独立于已知乳腺癌风险因素的乳腺癌的良好预测因子。识别与乳房X线摄影密度相关的基因将不仅识别与乳房密度相关的机制，而且具有检测与乳腺癌相关的基因的巨大潜力。我们建议在绝经后妇女中进行多阶段乳腺摄影密度GWAS（目的1）。作为癌症易感性遗传标记（CGEMS）项目的一部分，护士健康研究（NHS）中的绝经后乳腺癌病例和对照组完成了全基因组扫描。我们估计，我们将获得其中1,800名妇女的乳房X光密度数据。我们的初步分析将检查CGEMS项目（第1阶段）中纳入的250万个SNP（包括550，000个基因分型和其余插补）与乳腺摄影密度之间的关联。为了最大限度地减少假阳性和假阴性关联，我们将在NHS（第2阶段）的另外1，200名绝经后妇女中追踪第1阶段排名最高的7，600个SNP。在马约乳腺X线健康研究（MMHS）的3,000名绝经后受试者中，将对1,536个最有希望的SNP进行基因分型（第3阶段）。将对多阶段研究中出现的经验证的SNP进行生物学上合理的基因-环境相互作用评估（目标2）。NHS和MMHS都是人口统计学上相似的人群，具有血液样本，乳房X线摄影密度数据和关于乳腺癌风险因素的广泛暴露信息。我们还将评估来自Aim 1的经验证的SNP是否与NHS和乳腺癌和前列腺癌队列联盟（超过6，000例乳腺癌病例和对照）中的乳腺癌风险相关。拟议研究的结果将通过增加我们对乳腺生物学和乳腺癌病因学的理解来补充乳腺癌GWAS的结果。这是一个独特的，具有成本效益的，及时的建议，以确定乳腺密度和乳腺癌的新的遗传途径。鉴定与乳房X线摄影密度相关的基因将允许研究它们的功能，因为它与密度和乳腺癌有关，并为乳腺癌预防和治疗的新靶点开辟了可能性。公共卫生相关性：通过集中研究乳腺癌等具有多因素原因的复杂疾病的遗传因素，可以加强对该疾病遗传风险因素的阐明。乳腺摄影密度是一种高度可遗传的、可靠测量的数量性状，并且是独立于已知乳腺癌风险因素的乳腺癌的公认的强预测因子。这种多阶段全基因组乳腺摄影密度关联研究不仅将确定与乳腺密度相关的新位点，而且将通过增加我们对乳腺生物学和乳腺癌病因学的理解来补充乳腺癌的研究。

DESCRIPTION (provided by applicant): Mammographic density is one of the strongest risk factors for breast cancer. Women with the highest mammographic density are at a four- to six-fold greater risk of breast cancer than women with the lowest density. Recently for other chronic diseases (e.g., coronary artery disease), we have seen proof-of-principle that utilizing a reliably measured, heritable quantitative trait (e.g., circulating lipids) that is a strong risk factor for the outcome can identify novel loci for the disease that were not identified through genome-wide association studies (GWASs) of the outcome. Thus, studies of heritable phenotypes can uncover biological pathways that will lead to a better understanding of basic mechanisms of disease and may identify targets for intervention. In a similar paradigm, mammographic density is a highly heritable, reliably measured, quantitative trait and a well-established strong predictor of breast cancer independent of known breast cancer risk factors. Identifying genes associated with mammographic density will identify mechanisms related to not only breast density, but has immense potential to detect genes involved with breast cancer. We propose to conduct a multi-stage GWAS of mammographic density among postmenopausal women (Aim 1). As part of the Cancer Genetic Markers of Susceptibility (CGEMS) project, postmenopausal breast cancer cases and controls in the Nurses' Health Study (NHS) have whole genome scans completed. We estimate that we will have mammographic density data on 1,800 of these women. Our initial analysis will examine the association between 2.5 million SNPs (includes 550,000 genotyped and the remainder imputed) and mammographic density among women included in the CGEMS project (Stage 1). To minimize false positive and negative associations, we will pursue the highest-ranking 7,600 SNPs from Stage 1 in an additional 1,200 postmenopausal women from the NHS (Stage 2). The 1,536 most promising SNPs will be genotyped in 3,000 postmenopausal participants in the Mayo Mammography Health Study (MMHS) (Stage 3).Validated SNPs that emerge from the multi-stage study will be evaluated for biologically plausible gene-environment interactions (Aim 2). The NHS and MMHS are both well established cohorts of demographically similar populations with blood samples, mammographic density data and extensive exposure information on breast cancer risk factors. We will also evaluate if validated SNPs from Aim 1 are associated with breast cancer risk in the NHS and in the Breast and Prostate Cancer Cohort Consortium (with over 6,000 breast cancer cases and controls). The results of the proposed study will complement those from breast cancer GWASs by increasing our understanding of breast biology and etiology of breast cancer. This is a unique, cost-efficient, and timely proposal to identify novel genetic pathways underlying breast density and breast cancer. Identification of genes associated with mammographic density will allow for study of their function as it relates to density and breast cancer and opens up the possibility for novel targets of breast cancer prevention and treatment. PUBLIC HEALTH RELEVANCE: Elucidating the genetic components of complex diseases with multifactorial causes such as breast cancer can be enhanced through concentration on heritable risk factors for the disease. Mammographic density is a highly heritable, reliably measured, quantitative trait and a well- established strong predictor of breast cancer independent of known breast cancer risk factors. This multi-stage genome-wide association study of mammographic density will not only identify novel loci associated with breast density, but will complement the studies of breast cancer by increasing our understanding of breast biology and etiology of breast cancer.