Methods for high-dimensional data in HIV/CVD research

描述(由申请人提供)：最近的技术进步产生了大量的分子和细胞数据，为理解复杂的疾病病因和提供临床管理策略提供了前所未有的机会。然而，为了从这些丰富的数据存储中获得信息，我们需要开发合理和适当的分析技术。这一需求在人类免疫缺陷病毒(HIV)和心血管疾病(CVD)的交叉研究中尤其相关，这些研究的特点是病毒和宿主因素之间的一系列复杂的相互作用。这些因素包括病毒和宿主的遗传特征，以及热量代谢、免疫激活和炎症的标志，这些因素共同决定了对治疗的反应和总体疾病进展。由于存在大量可能提供信息的变量，而且这些变量之间的关系基本上没有明确的特征，因此对这些标志的全面评估提出了若干分析挑战。我们提出了一个多方面的战略，重点是综合统计方法的开发和应用。这些方法将使我们能够探索和描述与多种遗传、环境、人口和临床因素之间的复杂关系以及疾病进展衡量标准有关的新假设。具体地说，这一延续应用侧重于在两个背景下推进和应用统计学方法：第一，我们考虑基于人群的遗传关联研究，研究艾滋病毒感染者的先天免疫、脂肪因子、药物代谢和药物运输基因以及免疫重建、炎症和心血管疾病风险的标记；第二，我们解决与免疫恢复、炎症和心血管疾病风险相关的代谢和免疫学特征的调查。这项研究的具体目的是开发和评估：(1)潜在类别和混合建模范式，用于(A)发现和表征多位点基因-性状关联，(B)在候选基因调查中评估不可观察到的单倍型-性状关联；(2)分层混合模型和机器学习方法，用于(A)在资源有限的环境中监控定量生物标记物，以及(B)表征免疫重建和炎症的高维预测因子。影响：这项研究将导致创建适当的和仔细评估的分析工具，以从现有的丰富的分子和细胞数据中获得信息。最终，这项研究将提高我们将分子和细胞水平的数据转化为临床决策的能力，为个性化医学奠定基础。 公共卫生相关性：新获得的一系列关于基因多态和细胞水平免疫因素的数据有望为阐明复杂的疾病病因和提供临床管理策略提供前所未有的机会。使用人类免疫缺陷病毒(HIV)和心血管疾病(CVD)作为我们的模型系统，我们建议开发、评估和应用新的高维数据分析方法。最终，这些方法将使我们能够从海量的分子和细胞数据中获得信息，用于个性化的临床决策，从而成为转化医学的中心组成部分。

DESCRIPTION (provided by applicant): Recent technological advances have yielded vast quantities of molecular and cellular data, affording unprecedented opportunities to understand complex disease etiologies and to inform clinical management strategies. However, in order to derive information from these rich stores of data we need to develop sound and appropriate analytic techniques. This need is especially relevant in studies at the intersection of human immunodeficiency virus (HIV) and cardiovascular disease (CVD), which are characterized by an elaborate set of interactions among viral and host factors. These factors include viral and host genetic profiles, as well as markers of caloric metabolism, immune activation and inflammation, which work together to determine response to therapy and overall disease progression. A comprehensive assessment of these markers presents several analytical challenges owing to the large number of potentially informative variables and the largely uncharacterized relationship among them. We propose a multi-faceted strategy that focuses on the development and application of integrative statistical approaches. Such approaches will allow us to explore and characterize novel hypotheses relating to the complex relationships among multiple genetic, environmental, demographic, and clinical factors and measures of disease progression. Specifically, this continuation application focuses on advancing and applying statistical methods in two settings: first, we consider population-based genetic association studies of innate-immunity, adipokine, drug metabolism and drug transport genes and markers of immune reconstitution, inflammation and risk of CVD in HIV-infected individuals; and second, we address investigations of metabolic and immunologic profiles that associate with immune recovery, inflammation and risk of CVD. The Specific Aims of the proposed research are to develop and evaluate: (1) Latent class and mixture modeling paradigms for (a) discovering and characterizing multi-locus genotype-trait associations and (b) evaluating unobservable haplotype-trait associations in candidate-gene investigations; and (2) Hierarchical mixture models and machine learning approaches for (a) monitoring quantitative biomarkers in resource-limited settings and (b) characterizing high- dimensional predictors of immune reconstitution and inflammation. IMPACT: This research will lead to the creation of appropriate and carefully evaluated analytic tools to derive information from the rich array of molecular and cellular data now available. Ultimately, this research will advance our ability to translate molecular and cellular level data for clinical decision making, serving at the cornerstone of personalized medicine. PUBLIC HEALTH RELEVANCE: The newly available array of data on genetic polymorphisms and cellular level immune factors promises unprecedented opportunities to elucidate complex disease etiology and inform clinical management strategies. Using human immunodeficiency virus (HIV) and cardiovascular disease (CVD) as our model systems, we propose to develop, evaluate and apply new analytic approaches for high-dimensional data. Ultimately, these methods will allow us to derive information from the vast quantities of molecular and cellular data for personalized, clinical decisions and thus serve as a central component of translational medicine.