Methods and Software for High-dimensional Risk Prediction Research

高维风险预测研究方法和软件

• 批准号: 9924898
• 负责人: Qing Lu
• 金额: $ 29.52万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9924898
• 关键词: Address; Adopted; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease risk; Area; Biological Markers; Clinical; Clinical Data; Collaborations; Complex; Computer software; DNA sequencing; Data; Data Set; Development; Diffusion; Dimensions; Disease; Etiology; Family; Family Study; Future; Genes; Genetic; Genetic Heterogeneity; Genomics; Goals; Healthcare; Human Characteristics; Human Genome; Image; Individual; Lasso; Lead; Maps; Measures; Methodology; Methods; Modeling; Mutation; Nucleotides; Performance; Phenotype; Preventive treatment; Process; Proteomics; Research; Research Personnel; Risk; Trans-Omics for Precision Medicine; Translational Research; Variant; base; cost; design; disorder risk; epigenomics; genetic profiling; high dimensionality; high throughput technology; human disease; improved; multidimensional data; novel; precision medicine; programs; rare variant; risk prediction model; simulation; software development; success; tool; transcriptomics

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.

项目概要 使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 迈向精准医疗的重要一步。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况的阻碍，目前已知的风险预测因素还不够 准确预测大多数人类疾病。随着快速发展的高通量技术和不断 随着成本的降低，在大规模研究中收集不同类型的组学数据变得可行。虽然 这些研究生成的多级组学数据为进一步改进新的预测因子带来了巨大希望 现有模型、组学数据的高维性、人类疾病的异质病因学以及 不同级别的组学数据之间复杂的相互关系带来了巨大的分析挑战。新的 非常需要方法和软件来应对这些挑战，并促进持续和未来的高 维度风险预测研究。因此，该应用程序的目标是完成一个 使用组学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）。拟议的研究将整合核函数 以及 RF 中的空间自适应套索，使其适用于具有大量数据的高维数据 预测因子。此外，新框架能够利用家族设计来解决几个重要问题 （例如，遗传异质性）来预测复杂疾病，并将采用交叉扩散过程来 整合来自不同级别组学数据的信息。根据初步模拟结果，我们的中央 假设是所提出的框架比现有的框架获得了更准确和稳健的性能 方法。该项目的成功完成应该能够解决大规模数据分析所面临的挑战。 大量的组学数据，并推进高维风险的方法和软件开发 预测一般。新方法和软件在大规模AD数据集上的应用也可以 产生新颖的 ​​AD 风险预测模型，可以通过协作进一步复制和研究 研究。