Genomic and Transcriptomic Analysis of Mammographic Density

乳腺X线密度的基因组和转录组分析

• 批准号: 9917230
• 负责人: LAUREL A HABEL
• 金额: $ 50.16万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-11 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9917230
• 关键词: Biological; Biology; Breast; Breast Cancer Prevention; Breast Cancer Risk Factor; Clinical Data; Complex; Data; Databases; Detection; Development; Environment; Etiology; Gene Expression; Genes; Genetic; Genomics; Genotype; Genotype-Tissue Expression Project; Health; Heritability; Human; Individual; Information Networks; International; Intervention; Joints; Lead; Light; Mammary Gland Parenchyma; Mammographic Density; Mammography; Maps; Measurement; Measures; Methods; Modeling; Network-based; Outcome; Pathway Analysis; Pathway interactions; Phenotype; Preventive Intervention; Preventive therapy; Research; Resources; Risk Factors; Statistical Methods; Structure; Testing; Tissue Sample; Tissues; Training; Variant; Woman; base; breast density; cancer risk; cohort; cost efficient; data warehouse; density; effective therapy; gene discovery; genetic architecture; genome wide association study; human tissue; improved; innovation; insight; malignant breast neoplasm; novel; novel strategies; programs; secondary analysis; statistics; therapeutic target; trait; transcriptome; transcriptomics

ABSTRACT Mammographic density (MD) is one of the strongest established risk factors for breast cancer and has an estimated heritability of over 60%. Genome-wide association studies (GWAS) to date have explained only a small fraction of the heritability. We propose to combine gene expression and network information with GWAS data to augment the power to discover MD genes, and to gain insights into the biological mechanisms underlying MD and its association with breast cancer risk. Our specific aims are: Aim 1 Conduct a transcriptome-wide association study (TWAS) of MD in 24,192 women, and replicate findings in the Marker of Density (MODE) consortium. Aim 2 Conduct network analyses to discover gene sets (network modules) acting jointly on MD and elucidate the underlying biological pathways. We will construct tissue-specific gene co- expression networks using transcriptome data in normal human breast tissue samples from GTEx, and develop new statistical methods to integrate these tissue-specific networks to boost the power and accuracy of gene expression-based association tests. Aim 3 Evaluate associations of MD genes and modules with breast cancer risk using summary statistics from international breast cancer consortia, and individual-level GWAS data from 60K women in Kaiser's Research Program on Genes, Environment and Health (RPGEH) and independent replication set of 45K women in public GWAS data repositories. The proposed approach is expected to have substantially higher power than single-variant GWAS approaches because it rationally combines information, first across multiple SNPs using gene expression levels as an intermediary, and second across multiple genes using gene co-expression networks to model the correlation and interaction among genes. Moreover, gene- and network-based associations naturally provide a biological context, and are more easily interpreted than single SNP-based associations. The proposed research is innovative because we will develop new methods and a rational framework, based on gene expression and co-expression, to conduct gene-based and network-based association tests, which may be applied to study other traits. The results will improve our understanding of the genes and biological mechanisms underlying MD and its association with breast cancer risk, and may lead to the development of more effective therapies to prevent breast cancer.

摘要 乳房X线摄影密度(MD)是乳腺癌的最强危险因素之一，并具有 估计遗传力超过60%。到目前为止，全基因组关联研究(GWAS)只解释了一个 遗传力的一小部分。我们建议将基因表达和网络信息与GWAS相结合 数据增强了发现MD基因的能力，并获得了对生物机制的见解 潜在的MD及其与乳腺癌风险的关联。我们的具体目标是：目标1 在24,192名女性中进行的MD的转录组范围关联研究(TWAS)，并在Marker 密度(模式)联合体。目标2进行网络分析以发现起作用的基因集(网络模块) 共同研究MD并阐明潜在的生物学途径。我们将构建组织特异性基因联合 来自GTEx的正常人类乳腺组织样本中使用转录组数据的表达网络，以及 开发新的统计方法来整合这些组织特有的网络，以提高 基于基因表达的关联测试。目的3评价MD基因和模组与乳房的关系 使用来自国际乳腺癌联合会和个人水平的GWAs的汇总统计数据的癌症风险 来自凯泽基因、环境和健康研究计划(RPGEH)的6万名女性的数据 公共Gwas数据库中45K名妇女的独立复制集。建议的方法是 预计将比单一变量GWA方法具有更高的能力，因为它理性地 结合信息，首先使用基因表达水平作为中介，跨越多个SNP，然后 使用基因共表达网络对多个基因之间的相关性和相互作用进行建模 基因。此外，基于基因和网络的联系自然提供了一种生物学背景，而且 比单一的基于SNP的关联更容易解释。拟议的研究是创新的，因为我们将 开发基于基因表达和共表达的新方法和合理框架，以进行 基于基因和基于网络的关联测试，这可能被应用于研究其他性状。结果将会是 加深对MD的基因和生物学机制及其与疾病的关系的了解 乳腺癌的风险，并可能导致开发更有效的治疗方法来预防乳腺癌。