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Methods and Software for High-dimensional Risk Prediction Research

高维风险预测研究方法和软件

基本信息

批准号：
9975910
负责人：
Qing Lu
金额：
$ 25.54万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
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/9975910
关键词：
Address Adopted Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos 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 Analysis Individual Lasso Lead Maps Measures Methodology Methods Modeling Mutation Performance Phenotype Preventive treatment Process Proteomics Research Research Personnel Risk Single Nucleotide Polymorphism Trans-Omics for Precision Medicine Translational Research 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 treatment strategy

项目摘要

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.

项目总结