Biomedical Computing and Informatics Strategies for Infectious Disease Research

 描述（由申请人提供）：传染病研究的一个重要目标是开发易感性的遗传预测因子。我们在这奋进的成功将主要取决于信息学方法和软件，可用于理解高维遗传和基因组数据。该研究计划的目标是开发，评估，分发和支持新的和新颖的生物医学计算算法和开源软件，用于识别临床重要传染病结果的遗传预测因子的组合。该应用将针对通过高通量DNA测序鉴定的越来越多的罕见遗传变异。我们的临床应用将集中在HIV/AIDS临床试验中抗逆转录病毒反应的预测。我们在这里提出了一个高度创新的分层罕见变异折叠机（HRVCM）算法，用于识别和折叠跨基因区域（AIM 1）的罕见变异的组合。然后，我们将这些新的折叠HRVCM变量整合到我们流行的多因素重复性降低（MDR）方法中，该方法将与全基因组关联研究或GWAS（AIM 2）中的常见单核苷酸多态性（SNP）结合进行评估。我们的新HRVCM-MDR方法将首次使在传染病遗传研究中同时评估罕见和常见变异之间的非加性相互作用成为可能。我们将把这些新的和新颖的方法应用于来自近3000名参与艾滋病临床试验组（ACTG）研究的受试者的约1300万种罕见和常见变异，以评估含依法韦仑的抗逆转录病毒治疗（ART）方案（AIM 3）的病毒学失败风险。最后，我们将通过我们免费提供的MDR软件包（AIM 4）将所有方法作为开源发布给生物医学研究社区。

 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).