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

哈佛大学膳食生物标志物开发中心的数据分析核心

• 批准号: 10649591
• 负责人: Liming Liang
• 金额: $ 10.33万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-16 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10649591
• 关键词: Benchmarking; Bioinformatics; Biological Markers; Biometry; Calibration; Clinical Trials; Cohort Studies; Collaborations; Consultations; Data; Data Analyses; Data Coordinating Center; Data Set; Development; Diet; Dietary Intervention; Dietary Practices; Dietary intake; Discriminant Analysis; Disease; Disease Outcome; Dose; Drug Kinetics; Eating; Ensure; Epidemiology; Equation; Ethnic Population; Evaluation; Food; Food Analysis; Future; Health; High Performance Computing; Incidence; Individual; Intervention; Kinetics; Measurement; Mediation; Mendelian randomization; Metabolic; Methodology; Methods; Modeling; Modification; Multiomic Data; Performance; Phase; Plasma; Production; Protocols documentation; Quality Control; Sampling; Secure; Universities; Urine; Validation; Work; analytical method; biomarker development; biomarker discovery; biomarker performance; biomarker validation; clinical phenotype; cluster computing; cohort; data exchange; data harmonization; data management; data sharing; data standards; data submission; design; dietary; epidemiology study; feeding; genome wide association study; gut microbiota; high dimensionality; improved; interdisciplinary approach; interest; large datasets; machine learning method; metabolomics; multi-ethnic; multiple omics; novel; nutrition; nutritional epidemiology; power analysis; prospective; racial population; response; sex; statistics; synergism; Benchmarking; Bioinformatics; Biological Markers; Biometry; Calibration; Clinical Trials; Cohort Studies; Collaborations; Consultations; Data; Data Analyses; Data Coordinating Center; Data Set; Development; Diet; Dietary Intervention; Dietary Practices; Dietary intake; Discriminant Analysis; Disease; Disease Outcome; Dose; Drug Kinetics; Eating; Ensure; Epidemiology; Equation; Ethnic Population; Evaluation; Food; Food Analysis; Future; Health; High Performance Computing; Incidence; Individual; Intervention; Kinetics; Measurement; Mediation; Mendelian randomization; Metabolic; Methodology; Methods; Modeling; Modification; Multiomic Data; Performance; Phase; Plasma; Production; Protocols documentation; Quality Control; Sampling; Secure; Universities; Urine; Validation; Work; analytical method; biomarker development; biomarker discovery; biomarker performance; biomarker validation; clinical phenotype; cluster computing; cohort; data exchange; data harmonization; data management; data sharing; data standards; data submission; design; dietary; epidemiology study; feeding; genome wide association study; gut microbiota; high dimensionality; improved; interdisciplinary approach; interest; large datasets; machine learning method; metabolomics; multi-ethnic; multiple omics; novel; nutrition; nutritional epidemiology; power analysis; prospective; racial population; response; sex; statistics; synergism

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.

摘要/摘要 - 数据分析核心 数据分析核心（DAC）旨在通过A提供统计专业知识和编程支持 在设计和实施生物标志物项目（BP）期间的跨学科方法。 DAC会 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，并改善哈佛大学饮食生物标志物发展中心（DBDC）的协同作用 大学。 DAC将参加整个财团的计划活动，并提供咨询 为饮食干预制定共同的策略和方案。 DAC将与对方合作 DBDC和数据协调中心（DCC）在确定最终的统计分析策略 生物标志物分析和性能。具体目的是：目标1：开发和实施用于 生物标志物项目的生物标志物发现和验证。我们将设计数据分析 将饮食生物标志物表现与饮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裔研究中，基准生物标志物数据 样品。目的2：为生物标志物分析制定常见的统计分析策略和 性能适用于不同的DBDC。该核心将与其他DBDC和DCC一起使用 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的数据共享和提交到DCC。 此DAC将负责管理数据输入，清洁和分析我们的数据 DBDC。 DAC将与其他DBDC和DCC一起使用，以跨平台统一数据， 标准化DBDC的数据管理，QC和分析方法。目标4：界面营养， 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 校正模型和多词集成纳入了未来的营养流行病学研究 疾病结果。只要适当地 种族/族裔。作为哈佛大学DBDC跨学科团队的一部分，DAC将 开发和策划校准的生物标志物，精致的临床表型和摘要统计数据供将来使用 食物摄入量和与疾病事件和其他临床的前瞻性关联的流行病学分析 感兴趣的表型，允许测量误差纠正并与多摩学的进一步集成 在现有的大型队列研究中，数据集，例如肠道菌群和全基因组关联研究 哈佛大学。