Methods and Software for High-dimensional Risk Prediction Research

项目摘要 利用人类基因组发现和其他已建立的风险预测因子进行早期疾病预测是一种 迈向精准医学的关键一步。然而，开发临床上有用的风险预测的任务 模型受到目前证据状态的阻碍，其中目前已知的风险预测因子是不够的 准确地预测了大多数人类疾病。随着快速发展的高通量技术和不断变化的 随着成本的降低，在大规模研究中收集不同类型的基因组数据成为可能。而当 这些研究产生的多水平经济学数据为新的预测因子进一步改进提供了巨大的希望 现有的模型，高维的基因组数据，人类疾病的异质病因，以及 不同层次的基因组数据之间的复杂相互关系带来了巨大的分析挑战。新的 迫切需要方法和软件来应对这些挑战，并促进正在进行的和未来的高 维度风险预测研究。因此，此应用程序的目标是完成 使用基因组数据进行高维风险预测研究的随机场(RF)框架和软件，以及 然后将该框架应用于阿尔茨海默病(AD)。拟议的研究将整合一个核函数 和空间自适应套索转换为射频，使其适用于具有大量数据的高维数据 预测者。此外，新的框架能够利用家庭设计来解决几个重要的问题 (例如，遗传异质性)预测复杂的疾病，并将采用交叉扩散过程来 集成来自不同级别的基因组数据的信息。根据初步的模拟结果，我们的中央 假设提出的框架比现有的框架获得了更准确和更健壮的性能 方法：研究方法。该项目的成功完成应能解决大规模数据分析面临的挑战。 大量的基因组数据，并推进针对高维风险的方法和软件开发 大体上是预测。将新方法和软件应用于大规模AD数据集也可以 导致新的AD风险预测模型，这些模型可以通过协作进一步复制和研究 研究。

Project Summary The use of human genome discoveries and other established risk predictors for early disease prediction is an essential step towards precision medicine. However, the task of developing clinically useful risk prediction models is hampered by the present state of evidence, in which currently known risk predictors are insufficient for accurately predicting most human diseases. With rapidly evolving high-throughput technologies and ever- decreasing costs, it becomes feasible to collect diverse types of omic data in large-scale studies. While the multi-level omic data generated from these studies hold great promise for novel predictors to further improve existing models, the high-dimensionality of omic data, the heterogeneous etiology of human diseases, and the complex inter-relationships among various levels of omic data bring tremendous analytic challenges. New methods and software are in great need to address these challenges, and to facilitate ongoing and future high- dimensional risk prediction research. The goal of this application is thus to complete the development of a random field (RF) framework and software for high-dimensional risk prediction research using omic data, and then apply the framework to Alzheimer's disease (AD). The proposed research will integrate a kernel function and a spatial adaptive lasso into RF, making it applicable for high-dimensional data with a large number of predictors. Moreover, the new framework is able to utilize the family design to address several important issues (e.g., genetic heterogeneity) in predicting complex diseases, and will adopt a cross-diffusion process to integrate information from different levels of omic data. Based on preliminary simulation results, our central hypothesis is that the proposed framework attains a more accurate and robust performance than existing methods. The successful completion of this project should address analytical challenges faced by massive amounts of omic data, and advance the methodology and software development for high-dimensional risk prediction in general. The application of the new methods and software to large-scale AD datasets could also lead to novel AD risk prediction models that could be further replicated and investigated through collaborative research.