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: 10576856
• 负责人: Sara Lindstroem
• 金额: $ 43.83万
• 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/10576856
• 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; Epithelium; Estradiol; European ancestry; Fatty acid glycerol esters; First Births; Gene Expression; Genes; Genetic; Heritability; Heterogeneity; Hormones; Knowledge; Link; Mammary Gland Parenchyma; Mammographic Density; Mendelian randomization; 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; 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; instrument; malignant breast neoplasm; menopausal hormone therapy; 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.

抽象的 鉴于其与乳腺癌的密切相关，已提出乳腺X线摄影密度为替代物 乳腺癌的终点。我们以前已经进行了全基因组关联研究（GWAS） 乳房X线X型表型并鉴定出乳房X线学之间共享的多个遗传基因座 密度和乳腺癌。确实，作为一个连续，精确且高度可遗传的结果（约60％）， 乳房X线照相密度已证明是鉴定乳腺癌遗传危险因素的强大工具。 我们提出了一套遗传关联研究，旨在提高我们对遗传和 乳腺X线摄影密度和乳腺癌的环境预测指标。具体来说，我们将扩大我们的 以前对三个新领域的工作，包括（1）利用种系遗传和组织特异性基因表达 数据确定与乳房X线摄影密度相关的新型局部局部，（2）第一个全基因组基因环境 （GE）乳房X线摄影密度的相互作用研究和（3）首次Mendelian随机分组（MR）研究 乳房X线摄影密度。首先，我们将扩大对乳房X线摄影遗传结构的了解 密度通过进行最大的GWA和首次转录组关联研究（TWA）的密度 33,000名欧洲血统女性的乳房X线摄影密度。考虑到细胞异质性 乳腺组织，我们将进行细胞类型特异性的TWA。其次，我们将确定遗传变异和基因 其表达与已建立的环境风险因素相互作用，以改变乳房X线照相密度 对25,000名欧洲女性进行了第一个全基因组SNP GE相互作用和TWASXE研究 祖先。第三，我们将对提议影响乳房X线摄影密度的生物标志物进行MR分析 包括循环激素（SHBG，睾丸激素和雌二醇）和CRP。我们将利用新发布的 来自英国生物库的生物标志物数据导致鉴定了数百种相关的遗传变异 在这里提出的生物标志物，使我们能够生成强大的遗传工具进行MR分析。 我们的应用是对PA-17-239的响应：“现有数据的二次分析和集成到 阐明癌症风险和相关结果的遗传结构”。我们将利用来自 模式财团，该财团已组装了33,000多名女性的GWAS和乳房X线图密度数据 欧洲血统和环境风险因素数据的25,000名妇女的数据。通过拟议的 工作，我们将基于以前的观察，即乳腺X线摄影密度可以作为强大的代理 乳腺癌，并跟进我们在BCAC的发现，这是一项与超过120,000乳房的大规模合作 癌症病例。我们的目标的完成将导致识别乳房X线摄影密度的新风险因素 和乳腺癌，并阐明乳腺X线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 导致预防策略，这些策略专门针对人群的乳房密度降低。