Biomedical Computing and Informatics Strategies for Infectious Disease Research

传染病研究的生物医学计算和信息学策略

• 批准号: 9430380
• 负责人: Jason H. Moore
• 金额: $ 87.5万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9430380
• 关键词: AIDS clinical trial group; AIDS/HIV problem; Algorithmic Analysis; Algorithms; Anti-Retroviral Agents; Bioinformatics; Biomedical Computing; Biomedical Research; Clinical Trials; Communicable Diseases; Communities; Computer software; Computing Methodologies; Data; Detection; Dimensions; Disease Outcome; Disease susceptibility; Failure; Gene Frequency; Genes; Genetic; Genetic Models; Genetic Variation; Genetic study; Genome; Genomic Segment; Goals; Graph; High-Throughput DNA Sequencing; Human; Infectious Diseases Research; Informatics; Machine Learning; Measures; Methodology; Methods; Modeling; Population; Predisposition; Regimen; Research; Risk; Single Nucleotide Polymorphism; Statistical Data Interpretation; Time; Vaccination; Variant; antiretroviral therapy; base; biomedical informatics; clinical application; clinical predictors; design; efavirenz; gene interaction; genetic analysis; genetic association; genetic predictors; genetic variant; genome wide association study; genome-wide; genomic data; high dimensionality; innovation; method development; novel; open source; programs; public health relevance; rare variant; response; simulation; success; virology

 DESCRIPTION (provided by applicant): An important goal of infectious disease research is to develop genetic predictors of susceptibility. Our success in this endeavor will depend critically on the informatics methods and software that are available for making sense of high-dimensional genetic and genomic data. The goal of this research program is to develop, evaluate, distribute and support new and novel biomedical computing algorithms and open-source software for identifying combinations of genetic predictors of clinically important infectious disease outcomes. This application will target the growing body of rare genetic variants identified by high-throughput DNA sequencing. Our clinical application will focus on the prediction of antiretroviral response in clinical trials for HIV/AIDS. We propose here a highly innovative Hierarchical Rare Variant Collapsing Machine (HRVCM) algorithm for identifying and collapsing combinations of rare variants across gene regions (AIM 1). We will then integrate these new collapsed HRVCM variables into our popular Multifactor Dimensionality Reduction (MDR) method that will assess them in combination with common single-nucleotide polymorphisms (SNPs) from genome-wide association studies or GWAS (AIM 2). Our novel HRVCM-MDR approach will, for the first time, make it possible to assess non-additive interactions among sets of rare and common variants simultaneously in genetic studies of infectious diseases. We will apply these new and novel methods to approximately 13 million rare and common variants from nearly 3000 subjects that participated in an AIDS Clinical Trials Group (ACTG) study to evaluate risk for virologic failure with efavirenz-containing antiretroviral therapy (ART) regimens (AIM 3). Finally, we will release all methods as open source to the biomedical research community through our freely available MDR software package (AIM 4).