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

基本信息

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
项目类别：
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

项目摘要

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密切相关，乳房X光密度已被提议作为替代指标我们之前曾进行过乳腺癌的全基因组关联研究（GWAS）。乳房X线照相密度表型并确定了乳房X线照相之间共享的多个遗传位点事实上，作为一个连续、精确且高度遗传（~60%）的结果，乳房X光密度已被证明是识别乳腺癌遗传风险因素的有力工具。我们提出了一系列遗传关联研究，旨在增加我们对遗传和遗传的理解。具体来说，我们将扩大乳房X光密度和乳腺癌的环境预测因素。之前在三个新领域的工作，包括（1）利用种系遗传和组织特异性基因表达识别与乳房X线照相密度相关的新位点的数据，(2)第一个全基因组基因环境 (GE) 乳房 X 光密度的相互作用研究和 (3) 第一个孟德尔随机化 (MR) 研究首先，我们将扩展对乳房 X 光检查遗传结构的了解。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 光密度解释细胞异质性。其次，我们将鉴定遗传变异和基因。其表达与已确定的环境风险因素相互作用，通过以下方式改变乳房X光密度在 25,000 名欧洲女性中开展首次全基因组 SNP GE 相互作用和 TWASxE 研究第三，我们将对影响乳房 X 光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已导致数百种相关遗传变异的鉴定与这里提出的生物标志物一起，使我们能够生成用于 MR 分析的强大遗传工具。我们的申请是为了回应 PA-17-239：“对现有数据进行二次分析和整合，以阐明癌症风险和相关结果的遗传结构”。 MODE 联盟，收集了超过 33,000 名女性的 GWAS 和乳房 X 光密度数据在整个提议过程中，收集了 25,000 名女性的欧洲血统和环境风险因素数据。工作中，我们将建立在我们之前的观察基础上，即乳房X线照相密度可以作为一个强有力的代理乳腺癌，并跟踪我们在 BCAC 中的发现，BCAC 是一项与超过 120,000 名乳腺癌患者进行的大规模合作癌症病例的完成将导致确定乳房X光密度的新危险因素。和乳腺癌，并阐明乳房X线照相密度增加乳腺癌的机制识别和表征与高乳腺密度和乳腺癌相关的基因可能会增加风险。制定专门针对人群乳腺密度降低的预防策略。