Data Analysis Core for the Dietary Biomarkers Development Center at Harvard University

摘要/总结-数据分析核心 数据分析核心（DAC）旨在通过一个 在生物标志物项目（BP）的设计和实施过程中采用跨学科的方法。DAC将 积极参与BP和其他核心活动，同时最大限度地提高效率，避免重复 努力，并提高哈佛饮食生物标志物开发中心（DBDC）之间的协同作用 大学发展谘询委员会将参与整个联盟的规划活动，并在 制定饮食干预的共同战略和方案。DAC将与其他 DBDC和数据协调中心（DCC）确定最终的统计分析策略， 生物标志物分析和性能。具体目标是：目标1：制定和实施方法， 生物标志物项目的所有阶段的生物标志物发现和验证。我们将设计数据分析 将膳食生物标志物性能与膳食摄入量评估数据进行比较的策略， 在现有的饮食喂养试验和多种族人群的几项队列研究中的基准生物标志物数据 样品目的2：为生物标志物分析开发通用的统计分析策略， 适用于不同DBDC的性能。此核心将与其他DBDC和DCC一起工作， 确定生物标志物分析和性能评估的最终设计和分析策略。 目标3：管理和维护大型数据集，确保及时共享数据并提交给数据协调委员会。 该DAC将负责管理数据输入，清理和分析由我们的 DBDC DAC将与其他DBDC和DCC合作，协调跨平台的数据， 跨DBDC标准化数据管理、QC和分析方法。目标4：将营养、 流行病学，生物信息学/生物统计学和代谢组学，并确保尖端的测量误差 校正模型和多组学整合被纳入未来的营养流行病学研究， 疾病结果。在适当的情况下，所有分析将按性别和不同性别评估具体影响。 种族/民族团体。作为哈佛大学DBDC跨学科团队的一部分，DAC将 开发和管理校准的生物标志物、完善的临床表型和汇总统计数据，供将来使用 食物摄入量的流行病学分析以及与疾病发病率和其他临床 感兴趣的表型，允许测量误差校正和进一步整合多组学 数据集，如现有大型队列研究中的肠道微生物群和全基因组关联研究， 哈佛。

ABSTRACT/SUMMARY – DATA ANALYSIS CORE The Data Analysis Core (DAC) aims to provide statistical expertise and programming support via a transdisciplinary approach during the design and implementation of the Biomarkers Project (BP). The DAC will actively participate in the BP and other Core activities, while maximizing the efficiency, avoiding duplication of efforts, and improving the synergy among the Dietary Biomarker Development Center (DBDC) at Harvard University. The DAC will participate in the consortium-wide planning activities and provide consultations in developing common strategies and protocols for the dietary intervention. The DAC will work with the other DBDCs and Data Coordinating Center (DCC) in determining the final statistical analytical strategies for the biomarker analysis and performance. The specific aims are: Aim 1: To develop and implement methods for biomarker discovery and validation across all stages of the Biomarkers Project. We will devise data analysis strategies for comparing the dietary biomarker performance against the dietary intake assessment data and benchmark biomarker data in an existing dietary feeding trial and several cohort studies with multi-ethnic samples. Aim 2: To develop common statistical analytical strategies for the biomarker analysis and performance applicable across different DBDCs. This Core will work with other DBDCs and the DCC in determining the final design and analytical strategies for the biomarker analysis and performance evaluation. Aim 3: To manage and maintain large datasets and ensure timely data sharing and submission to the DCC. This DAC will be responsible for managing the data entry, cleaning and analyzing the data generated by our DBDC. The DAC will work together with other DBDCs and DCC to harmonize data across platforms, standardize data management, QC, and analytic methods across DBDCs. Aim 4: To interface nutrition, epidemiology, bioinformatics/biostatistics, and metabolomics, and ensure that cutting-edge measurement error correction models and multi-omics integrations are incorporated into future nutritional epidemiologic studies of disease outcomes. Whenever appropriate, all analyses will assess specific effects by sex and across different racial/ethnic groups. As part of the transdisciplinary team of the DBDC at Harvard University, the DAC will develop and curate calibrated biomarkers, refined clinical phenotypes and summary statistics for use in future epidemiological analyses of food intake and prospective associations with disease incidence and other clinical phenotypes of interest, allowing measurement error corrections and further integration with multi-omics datasets, such as gut microbiota and genome-wide association studies in existing large cohort studies at Harvard.