The genetic architecture of breast cancer risk factors and breast cancer

描述(申请人提供)：乳腺癌是一种复杂的疾病，既有遗传因素，也有非遗传因素。此外，许多已确定的乳房风险因素 癌症，包括乳房X光摄影密度、初潮年龄、自然绝经年龄、身高和身体质量指数(BMI)都受到很强的基因控制。我们最近发现有迹象表明，乳房X光照相密度和乳腺癌处于共同的基因控制之下，这突出了特定的因果路径。对这种共同的遗传起源的进一步表征将为乳腺癌病因学和生物学提供宝贵的见解，但由于缺乏足够的统计方法和大规模的经验数据集，因此无法进行这种分析。我们在这里建议开发新的统计方法，以量化和表征给定的乳腺癌风险因素和乳腺癌之间的总体共同基因起源。然后，我们将量化观察到的共同遗传起源的比例，这些比例可以通过已经识别的基因座来解释。这样的分析将告知可能的疾病途径。我们将开发基于方差分量理论的新的统计方法，以强有力地量化给定乳腺癌危险因素表型和乳腺癌之间的全基因组共享遗传起源(即所谓的跨性状遗传力)。然后，我们将研究已知的基因座可以解释总体共同遗传基础的比例。我们的方法只需要GWAS汇总统计数据作为输入，从而增强了它对基于大型联盟的数据集的适用性。我们将使用我们的方法来估计乳腺癌的共同遗传来源和五个乳腺癌风险因素中的每一个：乳房X线摄影密度、初潮年龄、自然绝经年龄、身高和BMI。我们已经获得了欧洲人和非裔美国人血统的数据集，这将使我们能够估计和比较种族之间的跨性状遗传力。对于欧洲血统的妇女，我们可以访问GWAS的汇总统计数据，这些统计数据基于15,000例乳腺癌病例和20,000名对照，5,000名接受乳房X光密度测量的妇女，以及7,000名初潮年龄、自然绝经年龄、身高和BMI的妇女。对于非裔美国人血统的妇女，我们可以获得基于3000例乳腺癌病例和2800名对照以及2400名妇女的体重指数和身高的Gwas汇总统计数据。这项研究应用描述了一种创新和成本效益高的方法来研究乳腺癌风险因素和乳腺癌之间的关联的原因机制。我们将开发新的方法来量化两个相关性状之间的共同遗传起源。我们的方法只需要GWAS汇总统计数据作为输入，从而增强了它对基于大型联盟的数据集的适用性。我们将通过发布用户友好的软件来公开我们的方法。最终，乳腺癌背后的遗传学特征将导致对乳腺癌生物学和病因学的新的和重要的见解。

DESCRIPTION (provided by applicant): Breast cancer is a complex disease with both genetic and non-genetic factors contributing to risk. In addition, many established risk factors for breast cancer including mammographic density, age at menarche, age at natural menopause, height, and body mass index (BMI) are under strong genetic control. We recently found indications that mammographic density and breast cancer are under shared genetic control, highlighting specific causal pathways. Further characterization of such shared genetic origin would provide invaluable insights in breast cancer etiology and biology, but lack of adequate statistical methods and large-scale empirical datasets has precluded such analysis. We here propose to develop new statistical methodology in order to quantify and characterize the overall shared genetic origin between a given breast cancer risk factor and breast cancer. We will then quantify the proportion of the observed shared genetic origin that can be explained by already identified loci. Such analysis will inform about possible disease pathways. We will develop novel statistical methodology based on variance component theory to robustly quantify the genome-wide shared genetic origin (so called cross-trait heritability) between a given breast cancer risk factor phenotype and breast cancer. We will then study what proportion of the overall shared genetic basis can be explained by already known loci. Our method requires only GWAS summary statistics as input, enhancing its applicability to large-scale consortia-based datasets. We will use our method to estimate the shared genetic origin of breast cancer and each of five breast cancer risk factors: mammographic density, age at menarche, age at natural menopause, height, and BMI. We have acquired datasets of European and African American ancestry that will allow us to estimate and compare cross-trait heritability between ethnicities. For women of European ancestry, we have access to GWAS summary statistics based on 15,000 breast cancer cases and 20,000 controls, 5,000 women with mammographic density measurements and 7,000 women for age at menarche, age at natural menopause, height and BMI. For women of African-American Ancestry, we have access to GWAS summary statistics based on 3,000 breast cancer cases and 2,800 controls as well as 2,400 women for BMI and height. This research application describes an innovative and cost-efficient approach to study the causal mechanisms underlying the associations between breast cancer risk factors and breast cancer. We will develop new methodology to quantify the shared genetic origin between two correlated traits. Our method requires only GWAS summary statistics as input, enhancing its applicability to large-scale consortia-based datasets. We will make our methodology publicly available by releasing user-friendly software. Ultimately, characterization of the genetics underlying breast cancer will lead to novel and important insights into breast cancer biology and etiology.