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

整合遗传、基因表达和环境数据，为乳房 X 光密度的生物学基础提供信息

• 批准号: 10341211
• 负责人: Sara Lindstroem
• 金额: $ 44.98万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-04 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10341211
• 关键词: Adipose tissue; Age; Age at Menarche; Alcohol consumption; Area; Biological; Biological Markers; Body mass index; Breast Cancer Risk Factor; Breast Feeding; Collaborations; Data; Environmental Risk Factor; Epithelial; Estradiol; European; Fatty acid glycerol esters; First Births; Gene Expression; Genes; Genetic; Heritability; Heterogeneity; Hormones; Knowledge; Lead; Light; Link; Mammary Gland Parenchyma; Mammographic Density; Mendelian randomization; Menopause; Outcome; Pathway interactions; Phenotype; Population; Prevention; Prevention strategy; Proxy; Research; Resources; Risk Factors; SHBG gene; Scanning; Series; Surrogate Endpoint; Testosterone; Tissue-Specific Gene Expression; Tissues; Woman; Work; base; biobank; breast density; cancer risk; cancer subtypes; cell type; density; follow-up; gene environment interaction; genetic architecture; genetic association; genetic predictors; genetic risk factor; genetic variant; genome wide association study; genome-wide; genomic locus; hormone therapy; instrument; malignant breast neoplasm; novel; parity; predictive modeling; response; risk prediction; secondary analysis; tool; transcriptome

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.

摘要 鉴于其与乳腺癌的密切联系，乳房X线摄影密度已被提议作为替代品 乳腺癌的终点。我们以前进行了全基因组关联研究（GWAS）， 乳腺摄影密度表型，并确定了多个遗传基因座，这些基因座在乳腺摄影之间共享 密度和乳腺癌之间的关系事实上，作为一个连续、精确且高度遗传（~60%）的结果， 乳房X线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。 我们提出了一套遗传关联研究，旨在增加我们对遗传和 乳腺摄影密度和乳腺癌的环境预测因子。具体而言，我们将扩大我们的 以前的工作包括三个新的领域，包括（1）利用种系遗传和组织特异性基因表达 数据，以确定新的基因座与乳腺摄影密度，（2）第一个全基因组基因环境 (GE)乳腺摄影密度的相互作用研究和（3）第一个孟德尔随机化（MR）研究， 乳房摄影密度首先，我们将扩展我们对乳腺X线摄影的遗传结构的知识， 通过进行最大的GWAS和第一个全转录组关联研究（TWAS）， 33，000名欧洲血统女性的乳房X线摄影密度。为了解释 乳腺组织，我们将进行细胞类型特异性TWAS。其次，我们将识别遗传变异和基因 其表达与确定的环境风险因素相互作用，通过以下方式改变乳腺摄影密度： 在25，000名欧洲女性中进行了第一次全基因组SNP GE相互作用和TWASxE研究， 祖先第三，我们将对影响乳腺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CRP。我们将利用新发布的 来自英国生物银行的生物标志物数据，这导致了数百种遗传变异的鉴定， 与这里提出的生物标志物，使我们能够产生强大的遗传工具的MR分析。 我们的申请是为了响应PA-17-239：“现有数据的二次分析和集成， 阐明癌症风险和相关结果的遗传结构”。我们将利用来自 MODE联盟，该联盟收集了超过33，000名妇女的GWAS和乳腺摄影密度数据 欧洲血统和环境风险因素数据的一个子集的25,000名妇女。整个拟定 工作，我们将建立在我们以前的观察，乳房摄影密度可以作为一个强大的代理， 乳腺癌，并跟进我们在BCAC的发现，一个大规模的合作，超过120,000乳腺癌 癌症病例我们的目标的完成将导致识别乳腺X线摄影密度的新风险因素 和乳腺癌，并阐明了乳腺摄影密度增加乳腺癌的机制， 风险识别和表征与高乳腺密度和乳腺癌相关的基因， 导致专门针对人群乳腺密度降低的预防战略。