SOFTWARE FOR LARGE-SCALE INFERENCE OF THE GENETICS OF LIFESTYLE MEASURES, BIOMARKERS, AND COMMON AND RARE DISEASES

全球大规模人群生物库，疾病聚焦NHGRI基因组测序计划 (GSP)美国的All of Us Precision Medicine Initiative项目将产生大量基因组 与疾病结果和其他健康测量相结合的数据集。这些基因组研究将 识别与健康和疾病相关的基因组变异。然而，他们的联系在所有的背景下， 如果对数据进行单独分析，可能发现的关联仍不清楚。人们越来越 大多数性状是多基因的。此外，人们越来越认识到多效性是普遍存在的。 由于隐私问题，共享所有可能的基因型和表型数据具有挑战性。方法可以 对汇总水平数据进行推断，例如p值、效应量估计和频率，将有助于我们 了解人类疾病和健康的遗传学。在这里，我们建议开发软件， 大规模推断生活方式措施，生物标志物，常见和罕见的遗传学 疾病实现这一目标需要医学和群体遗传学、统计方法 开发和管理大型数据库的专业知识。该项目有三个主要目标。 首先，我们将创建全球生物库引擎：一个强大的，交互式的网络平台，用于推断 生活方式测量、生物标志物、常见和罕见疾病的遗传学。我们将通过以下方式扩展功能 实施质量控制可视化和标记变体和表型的方法。我们将添加 研究设计工具，使用经验数据来估计统计功效，并为 联合分析多种表型同时控制假阳性和假阴性的统计模型 调查结果。其次，我们将提高全球生物库引擎的性能、可扩展性和可访问性 以促进未来的人口生物库和针对常见和罕见疾病。我们将创建一个托管， 安全、经济高效的基于云的社区资源，并设计一个数据库系统， 遗传关联研究的加载时间从数小时到数分钟，并允许统计数据流 算法直接转化为基因数据。最后，我们将改进基因组解释，可视化和数据 通过实施新颖的分析，共享以大幅提高翻译发现的速度 方法.我们将支持新的变体注释方法，并整合编码和非编码信息， 包括来自大规模表观基因组学研究的数据，用于变体和基因水平的推断。落实 新的贝叶斯统计模型实现概率编程语言，稀疏规范 相关分析和截断奇异值分解。PI里瓦斯和他的团队 与NIH资助的财团的经验，他们致力于NIH的整体使命及其资助的 研究人员发现新的知识，将导致更好的健康为每个人。

Large-scale population biobanks around the world, the disease focused NHGRI Genome Sequencing Program (GSP), and the United States’ All of Us Precision Medicine Initiative project will generate massive genomic datasets combined with disease outcomes, and other health measurements. These genomic studies will identify genomic variants relevant to health and disease. However, their association in the context of all possible associations identified will remain unclear if the data are separately analyzed. There is a growing recognition that most traits are polygenic. In addition, it is increasingly appreciated that pleiotropy is pervasive. Due to privacy concerns, it is challenging to share all possible genotype and phenotype data. Methods that can perform inference on summary level data, e.g. p-values, effect size estimates, and frequency, will facilitate our understanding of the genetics of human diseases and health. Here, we propose to develop software for large-scale inference of the genetics of lifestyle measures, biomarkers, and common and rare diseases. Achieving this goal requires expertise in medical and population genetics, statistical methods development, and expertise in management of large-scale databases. The project has three main objectives. First, we will create Global Biobank Engine: a powerful, interactive web platform for inference of the genetics of lifestyle measures, biomarkers, common and rare diseases. We will expand the features by implementing quality control visualizations and methods for flagging variants and phenotypes. We will add tools for study design that use empirical data to estimate statistical power, and create a flexible framework for statistical models that jointly analyze multiple phenotypes while controlling for false positive and negative findings. Secondly, we will improve Global Biobank Engine performance, scalability, and accessibility to facilitate future population biobanks and targeted common and rare disease. We will create a hosted, secure, and cost-effective cloud-based community resource, and design a database system that reduces the loading time for genetic association studies from hours to minutes and allows for streaming of statistical algorithms directly to genetic data. Lastly, we will improve genomic interpretation, visualization, and data sharing to dramatically increase the rate of translational discoveries by implementing novel analysis methods. We will support new variant annotation methods and integrate coding and non-coding information, including data from large-scale epigenomics studies, for variant and gene level inference. We will implement new Bayesian statistical models implemented in probabilistic programming languages, sparse canonical correlation analysis, and truncated singular value decomposition. PI Rivas and his team have ample experience with NIH-funded consortia, and they are dedicated to the overall mission of NIH and its funded investigators to uncover new knowledge that will lead to better health for everyone.