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

摘要/摘要--数据分析核心 数据分析核心(DAC)旨在通过 在设计和实施生物标记物项目(BP)期间采用跨学科方法。DAC将 积极参与BP和其他核心活动，同时最大化效率，避免重复 努力，提高哈佛饮食生物标记物发展中心(DBDC)之间的协同效应 大学。发审会将参与整个财团的规划活动，并在 制定共同的饮食干预策略和方案。DAC将与另一个一起工作 DBDC和数据协调中心(DCC)确定最终的统计分析战略 生物标志物分析和性能。具体目标是：目标1：制定和实施以下方法 生物标记物项目所有阶段的生物标记物发现和验证。我们将设计数据分析 将膳食生物标记物的表现与膳食摄入量评估数据和 现有饮食喂养试验和多种族队列研究中的基准生物标记物数据 样本。目标2：制定生物标志物分析的通用统计分析策略 适用于不同DBDC的性能。此核心将与其他DBDC和DCC在 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，并确保及时分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、质量控制和分析方法。目标4：连接营养， 流行病学、生物信息学/生物统计学和代谢组学，并确保尖端测量误差 校正模型和多组学整合将被纳入未来的营养流行病学研究 疾病后果。在适当的时候，所有的分析都将评估性别和不同性别之间的具体影响。 种族/民族群体。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、改进的临床表型和汇总统计数据以供将来使用 食物摄入量的流行病学分析及其与疾病发病率和其他临床症状的前瞻性关联 感兴趣的表型，允许测量误差修正和与多组学的进一步集成 数据集，如肠道微生物区系和现有大型队列研究中的全基因组相关性研究，请访问 哈佛大学。

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.