Whole Genome Association Study of Mammographic Density

描述(申请人提供)：乳房X光摄影密度是乳腺癌的最大危险因素之一。乳房X光检查密度最高的女性患乳腺癌的风险是密度最低的女性的四到六倍。最近，对于其他慢性疾病(例如，冠状动脉疾病)，我们已经看到了原则证明，利用可靠测量的、可遗传的数量性状(例如，循环脂类)，这是影响结果的强烈风险因素，可以识别通过结果的全基因组关联研究(GWASs)没有确定的疾病的新基因座。因此，对可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，并可能确定干预的目标。在类似的范例中，乳房X光摄影密度是一种高度可遗传的、可靠测量的数量性状，是独立于已知乳腺癌危险因素的乳腺癌强有力的预测因子。识别与乳房X光照相密度相关的基因将不仅识别与乳房密度有关的机制，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一项多阶段的乳房X光摄影密度测量(目标1)。作为癌症遗传易感性标记(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已经完成了全基因组扫描。我们估计，我们将拥有其中1800名女性的乳房X光检查密度数据。我们的初步分析将在CGEMS项目(阶段1)中检查250万个SNPs(包括55万个基因分型和其余的推测)与乳房X光检查密度之间的关系。为了最大限度地减少假阳性和阴性关联，我们将在NHS的另外1,200名绝经后妇女(阶段2)中寻找第一阶段中排名最高的7,600个SNP。在梅奥乳房X光摄影健康研究(MMHS)(第3阶段)中，将对3000名绝经后参与者进行1536个最有希望的SNPs的基因分型。从多阶段研究中出现的经过验证的SNPs将被评估为生物学上可信的基因-环境相互作用(Aim 2)。NHS和MMHS都是人口学上相似的人群的可靠队列，拥有血液样本、乳房X光检查密度数据和关于乳腺癌风险因素的广泛暴露信息。我们还将在NHS和乳腺癌和前列腺癌队列联盟(有6000多例乳腺癌病例和对照)中评估来自AIM 1的有效SNPs是否与乳腺癌风险有关。拟议的研究结果将通过增加我们对乳腺癌生物学和乳腺癌病因学的理解来补充乳腺癌GWAS的结果。这是一项独特的、具有成本效益的、及时的建议，旨在确定乳房密度和乳腺癌潜在的新的遗传途径。识别与乳房X光照相密度相关的基因将有助于研究它们与密度和乳腺癌的关系，并为乳腺癌预防和治疗的新靶点开辟可能性。公共卫生相关性：通过集中研究疾病的可遗传风险因素，可以加强对乳腺癌等具有多因素原因的复杂疾病的遗传成分的阐明。乳腺X光摄影密度是一种高度可遗传、可靠测量的数量性状，是独立于已知乳腺癌危险因素的乳腺癌强有力的预测因子。这种多阶段全基因组范围的乳腺摄影密度关联研究不仅将确定与乳腺密度相关的新的基因座，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.