Integration of genetic, gene expression and environmental data to inform biological basis of mammographic density

摘要 鉴于其与乳腺癌的密切联系，乳房X光照相密度已被建议作为替代指标。 乳腺癌的终点。我们以前曾进行过全基因组关联研究(GWAS) 乳房X光摄影密度表型和识别的多个遗传位点在乳房X光摄影之间共享 密度和乳腺癌。事实上，作为一个连续、精确和高度可遗传的结果(~60%)， 乳房X光摄影密度已被证明是确定乳腺癌遗传风险因素的有力工具。 我们提出了一套基因关联研究，旨在增加我们对基因和 乳房X光照相密度的环境预测因素，从而预测乳腺癌。具体地说，我们将扩大我们的 之前对三个新领域的工作包括(1)利用生殖系遗传和组织特异性基因表达 识别与乳房X光照相密度相关的新基因座的数据，(2)第一个全基因组基因环境 (GE)乳房摄影密度的相互作用研究和(3)第一次孟德尔随机化(MR)研究 乳房X光摄影密度。首先，我们将扩展我们对乳房X光检查的遗传结构的知识 通过进行最大的GWAS和第一个转录组范围的关联研究(TWAS)来提高密度 3.3万名欧洲血统妇女的乳房X光照相密度。为了解释细胞的异质性， 乳房组织中，我们将进行细胞类型特异性TWA术。第二，我们将识别遗传变异和基因 其表达与既定的环境风险因素相互作用，通过以下方式改变乳房X光照相密度 在25,000名欧洲女性中进行首次全基因组SNP GE相互作用和TWASxE研究 血统。第三，我们将对影响乳房X光摄影密度的生物标志物进行mr分析。 包括循环激素(SHBG、睾酮和雌二醇)和C反应蛋白。我们将利用新发布的 来自英国生物库的生物标记物数据，已导致识别出数百个相关的遗传变异 有了这里提出的生物标记物，我们就可以为MR分析生成强大的基因工具。 我们的应用程序响应PA-17-239：“对现有数据进行二次分析和集成，以 阐明癌症风险和相关后果的遗传结构“。我们将利用来自 MODE联盟，它收集了超过33,000名女性的GWAS和乳房X光密度数据 欧洲血统和环境风险因素数据的一个子集的25,000名妇女。在整个建议中 工作中，我们将建立在我们之前的观察结果的基础上，即乳房X光检查密度可以作为 乳腺癌，并在BCAC上跟进我们的发现，这是一个与超过12万个乳房的大规模合作 癌症病例。完成我们的目标将导致确定乳房X光检查密度的新风险因素 和乳腺癌，并阐明了乳房X光摄影密度增加乳腺癌的机制 风险。识别和表征与高乳房密度和乳腺癌相关的基因可能 导致制定专门针对人群中乳房密度降低的预防战略。

ABSTRACT Given its strong association with breast cancer, mammographic density has been proposed as a surrogate endpoint for breast cancer. We have previously conducted genome-wide association studies (GWAS) of mammographic density phenotypes and identified multiple genetic loci that are shared between mammographic density and breast cancer. Indeed, as a continuous, precise and highly heritable (~60%) outcome, mammographic density has proven a powerful tool for identifying genetic risk factors for breast cancer. We propose a suite of genetic association studies aiming to increase our understanding of genetic and environmental predictors of mammographic density and thereby breast cancer. Specifically, we will expand our previous work to three novel areas including (1) leveraging germline genetic and tissue-specific gene expression data to identify novel loci associated with mammographic density, (2) the first genome-wide gene-environment (GE) interaction studies of mammographic density and (3) the first Mendelian Randomization (MR) studies of mammographic density. First, we will expand our knowledge of the genetic architecture of mammographic density by conducting the largest GWAS and the first transcriptome-wide association study (TWAS) of mammographic density in 33,000 women of European ancestry. To account for the cellular heterogeneity in breast tissue, we will conduct cell type-specific TWAS. Second, we will identify genetic variants and genes whose expression interact with established environmental risk factors to alter mammographic density by conducting the first genome-wide SNP GE interaction and TWASxE studies in 25,000 women of European ancestry. Third, we will conduct MR analysis for biomarkers proposed to influence mammographic density including circulating hormones (SHBG, testosterone and estradiol) and CRP. We will leverage newly released biomarker data from UK Biobank which has led to the identification of hundreds of genetic variants associated with the biomarkers proposed here, allowing us to generate strong genetic instruments for MR analysis. Our application is in response to PA-17-239: “Secondary Analysis and Integration of Existing Data to Elucidate the Genetic Architecture of Cancer Risk and Related Outcomes”. We will capitalize on data from the MODE consortium, which has assembled GWAS and mammographic density data on more than 33,000 women of European ancestry and environmental risk factor data for a subset of 25,000 women. Throughout the proposed work, we will build on our previous observation that mammographic density can serve as a powerful proxy for breast cancer, and follow up our findings in BCAC, a large-scale collaboration with more than 120,000 breast cancer cases. Completion of our aims will lead to identification of novel risk factors for mammographic density and breast cancer, and shed light on mechanisms by which mammographic density increases breast cancer risk. Identifying and characterizing genes associated with high breast density and breast cancer could lead to prevention strategies that specifically target breast density reductions in the population.