Bioinformatics, Biostatistics, and Data Management Core

项目总结（APOE U19核心G：生物信息学、生物统计学和数据管理核心）

PROJECT SUMMARY (APOE U19 Core G: Bioinformatics, Biostatistics, and Data Management Core) The complexity of multi-omics as well as cognitive/functional data generated by the Projects and Cores in this U19 application and the subsequent mandate for cutting-edge bioinformatics and biostatistical analyses demand a dedicated Bioinformatics, Biostatistics, and Data Management Core (Core G). The Projects and Cores span a wide range of analytical areas including population genetics, structural biology, proteomics, lipidomics, metabolomics, transcriptomics, and digital pathology. As such, during the designing stage of the proposed U19 program, the Core G worked extensively with every Project and Core team to: 1) harmonize and standardize sample metadata between different institutions; 2) ensure consistency and adequacy of analytic approaches across projects and cores; 3) determine optimal study design for all projects and cores to allow rigorous testing of their proposed hypotheses; and 4) identify analytic challenges for which novel algorithms and methods need to be developed by Core G. Core G will coordinate and perform all U19-related study design, data QC and analytics, and data/resource management activities. More importantly, Core G will take on the significant challenge of integrating and interpreting multi-omics and genotypic/phenotypic data to uncover molecular mechanisms and pathways driving age-related cognitive decline in the context of our novel ApoE Cascade Hypothesis (ACH). Core G plays a critical role in this proposed U19 program because it acts not only as the coordinator and synergizer between Cores and Projects to provide adequate analytic and data management support for all other Projects and Cores, but also as an investigative team to unveil the ACH-based aging mechanism using the multi-modalities and multi-omics datasets to be generated by the entire U19 program. Core G will ensure database, bioinformatics, and biostatistics integrity for study design and data analyses across U19 teams, and at the same time address the significant challenge of multi-omics data integration and interpretation by developing novel methodologies including the state-of-art machine learning and deep learning approaches. The Core G staff have a track record of both extensive interactions/collaborations with the entire U19 team and independent investigations in aging and AD research, and are thus well situated to lead the database and analytic activities of the proposed U19 program as well as the search of ACH-based aging mechanism.