Methods for high-dimensional data in HIV/CVD research

HIV/CVD 研究中的高维数据方法

• 批准号: 8423746
• 负责人: Andrea S Foulkes
• 金额: $ 38.09万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-03 至 2016-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8423746
• 关键词: Address; Award; Biological Markers; Biological Models; Biometry; Candidate Disease Gene; Cardiovascular Diseases; Clinical; Clinical Management; Clinical Research; Collaborations; Complex; Computer software; Data; Data Collection; Development; Disease; Disease Outcome; Disease Progression; Drug Transport; Dyslipidemias; Environment; Environmental Risk Factor; Etiology; Genes; Genetic; Genetic Polymorphism; Genotype; Goals; HIV; Haplotypes; Immune; Immunologic Markers; Immunologics; Immunology; Individual; Inflammation; Integration Host Factors; Investigation; Laboratories; Lead; Lipids; Machine Learning; Measures; Medicine; Metabolic; Metabolic Marker; Metabolism; Methods; Microbiology; Modeling; Molecular; Monitor; Natural Immunity; Peer Review; Pharmacology; Publications; Recovery; Research; Research Personnel; Resources; Software Tools; Statistical Computing; Statistical Methods; Techniques; Testing; Textbooks; Time; Translating; Translational Research; Viral; Work; adaptive immunity; cardiovascular disorder risk; clinical decision-making; drug metabolism; genetic association; genetic profiling; genome wide association study; immune activation; insight; molecular array; novel; open source; population based; professor; public health relevance; reconstitution; response; sound; statistics; success; tool; trait; translational medicine

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.

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，为了解复杂的疾病病因和为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取信息，我们需要开发合理且适当的分析技术。这种需求在人类免疫缺陷病毒（HIV）和心血管疾病（CVD）交叉研究中尤其重要，其特点是病毒和宿主因素之间存在一系列复杂的相互作用。这些因素包括病毒和宿主遗传特征，以及热量代谢、免疫激活和炎症的标志物，它们共同决定对治疗的反应和整体疾病进展。由于存在大量潜在信息变量以及它们之间的关系很大程度上未表征，对这些标记的全面评估提出了一些分析挑战。我们提出了一项多方面的战略，重点关注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表征与多种遗传、环境、人口和临床因素以及疾病进展测量之间的复杂关系相关的新假设。具体来说，该延续申请侧重于在两种情况下推进和应用统计方法：首先，我们考虑基于人群的先天免疫、脂肪因子、药物代谢和药物转运基因以及免疫重建、炎症和 CVD 风险标记物的遗传关联研究HIV感染者；其次，我们研究与免疫恢复、炎症和心血管疾病风险相关的代谢和免疫学特征。拟议研究的具体目标是开发和评估：（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型-性状关联以及（b）评估候选基因中不可观察的单倍型-性状关联调查； (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测定量生物标志物，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且经过仔细评估的分析工具，以便从现有的丰富的分子和细胞数据中获取信息。最终，这项研究将提高我们将分子和细胞水平数据转化为临床决策的能力，成为个性化医疗的基石。