Whole Genome Association Study of Mammographic Density

描述（由申请人提供）：乳房 X 光密度是乳腺癌最强的危险因素之一。乳房X线照相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），我们已经看到了原理验证，即利用可靠测量的、可遗传的数量性状（例如，循环脂质）作为结果的强风险因素，可以识别出通过结果的全基因组关联研究（GWAS）未识别出的疾病的新位点。因此，对遗传表型的研究可以揭示生物学途径，从而更好地了解疾病的基本机制，并可能确定干预目标。在类似的范例中，乳房X光密度是一种高度遗传的、可靠测量的数量性状，并且是独立于已知乳腺癌危险因素的公认的强有力的乳腺癌预测因子。识别与乳房X光密度相关的基因不仅可以识别与乳房密度相关的机制，而且具有检测与乳腺癌有关的基因的巨大潜力。我们建议对绝经后妇女进行乳房 X 光密度的多阶段 GWAS（目标 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已完成全基因组扫描。我们估计我们将获得其中 1,800 名女性的乳房 X 光密度数据。我们的初步分析将检查 CGEMS 项目（第一阶段）中女性的 250 万个 SNP（包括 550,000 个基因分型和其余的估算值）与乳房 X 光密度之间的关联。为了最大限度地减少假阳性和阴性关联，我们将在 NHS（第 2 阶段）的另外 1,200 名绝经后女性中寻找第 1 阶段中排名最高的 7,600 个 SNP。梅奥乳腺 X 光检查健康研究 (MMHS)（第 3 阶段）将在 3,000 名绝经后参与者中对 1,536 个最有希望的 SNP 进行基因分型。多阶段研究中出现的经过验证的 SNP 将针对生物学上合理的基因-环境相互作用进行评估（目标 2）。 NHS 和 MMHS 都是人口统计学相似人群的完善队列，拥有血液样本、乳房 X 光密度数据和有关乳腺癌危险因素的广泛暴露信息。我们还将评估来自 Aim 1 的经过验证的 SNP 是否与 NHS 以及乳腺癌和前列腺癌队列联盟（拥有超过 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了解来补充乳腺癌 GWAS 的结果。这是一项独特、经济高效且及时的提议，旨在确定乳腺密度和乳腺癌的新遗传途径。鉴定与乳房X线密度相关的基因将有助于研究它们与密度和乳腺癌相关的功能，并为乳腺癌预防和治疗的新靶标开辟了可能性。公共卫生相关性：通过关注该疾病的遗传风险因素，可以加强阐明乳腺癌等多因素原因造成的复杂疾病的遗传成分。乳房X线密度是一种高度遗传的、可靠测量的数量性状，并且是独立于已知乳腺癌危险因素的公认的强有力的乳腺癌预测因子。这项关于乳房X光密度的多阶段全基因组关联研究不仅将识别与乳房密度相关的新位点，而且将通过增加我们对乳房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.