Data Analysis and Management

数据管理和分析核心(核心D)将在研究设计以及数据管理和分析的所有方面为项目和其他核心提供支助。本POL侧重于通过成像技术表征特定的肥胖症表型，在生活方式和生物标记物变量中识别表型的预测因素，这些预测因素来自不同生物维度(基因组、代谢组学、微生物组)的测量，并测试预测因素作为替代措施与 患上几种癌症的风险。研究对象是著名的多种族队列(MEC)研究的参与者。核心D的经验丰富的生物统计学家和生物信息学家共同拥有丰富的MEC数据和设计经验，以及支持P01目标所需的数据和分析类型。具体目标包括：(1)加强MEC复杂的信息系统，以确定可进行招聘的队列成员，通过研究跟踪他们的进展情况，并监测接收情况， 所有生物材料的储存和运输；(2)对来自遗传变异、代谢组学和肠道微生物组的高通量数据进行预处理，以降低维度，提供最合适的分析数据；以及(3)与项目负责人一起，为本P01申请的每个研究项目制定和执行分析计划。特别是，将使用适当的统计工具和软件来寻找正在研究的肥胖症表型的最具预测性的模型，并用于这些预测因素与癌症风险的相关性研究。将在预测和关联模型中检查和测试各种族群体的异质性。

The Data Management and Analysis Core (Core D) will provide support for the projects and other cores in all aspects of study design and data management and analysis. This POl focuses on the characterization of specific adiposity phenotypes by imaging techniques, the identification of predictors of the phenotypes among lifestyle and biomarker variables derived from measurements in various biological dimensions (genefics, metabolomics, microbiome), and testing the association of the predictors, as proxy measures, with the risk of several cancers. The study subjects are participants in the well-established Multiethnic Cohort (MEC) study. The experienced biostatisticians and bioinformaticians of Core D collectively have extensive experience with the MEC data and design, and the types of data and analyses needed to support the aims of the P01. Specific aims include (1) to enhance the complex MEC informatics system to identify cohort members to approach for recruitment, to track their progress through the study, and to monitor receipt, storage and shipment of all biologic materials, (2) to pre-process high through-put data from genetic variation, metabolomics and gut microbiome in order to reduce the dimensionality and provide the most appropriate data for analysis, and (3) to develop and execute analysis plans for each of the research projects of this P01 application, in conjunction with the Project Leaders. In particular, appropriate statistical tools and software will be used to find the most predictive models of the adiposity phenotype under study and for the association studies of these predictors with cancer risk. Heterogeneity will be examined and tested across the ethnic groups in the prediction and association models.