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

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