Human Translational Bioinformatics Core

人类转化生物信息学核心

• 批准号: 10713763
• 负责人: Hooman Allayee
• 金额: $ 9.17万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10713763
• 关键词: Basic Science; Bioinformatics; Biological; Biological Markers; Biological Models; Candidate Disease Gene; Cardiometabolic Disease; Cells; Clinical; Clinical Research; Communities; Computer Analysis; Computer software; Coronary Arteriosclerosis; Data; Data Set; Databases; Disease; Ensure; Epidemiology; Genes; Genetic; Genetic Complementation Test; Genetic study; Genomics; Genotype; Goals; Health; Hospitals; Human; Human Activities; Human Resources; In Vitro; Inbred Strains Mice; Intuition; Knowledge; Leadership; Molecular; Multiomic Data; Mus; Online Systems; Outcome; Pathway interactions; Population; Proteomics; Publishing; Research; Research Personnel; Resources; Risk Factors; Role; Sample Size; Series; Sex Differences; System; Systems Biology; Translating; Update; Validation; Variant; Visualization software; Woman; analytical tool; biomarker identification; candidate identification; cardiometabolic risk; cardiometabolism; cohort; computer infrastructure; cost effective; experience; experimental study; genetic association; genome wide association study; human data; in silico; large scale data; men; metabolomics; mouse model; multiple data sources; online resource; open source tool; phenome; programs; searchable database; sexual dimorphism; success; targeted biomarker; tool; trait; transcriptomics; web based interface

PROJECT SUMMARY The overall objective of this SCORE is to elucidate sex differences in cardiometabolic disease and its risk factors and treatments using genetically modified mouse models, inbred mouse strains, and in vitro or cell-based experiments. However, another important component of the SCORE is to translate findings from model systems to humans. The Human Translational Bioinformatics Core will fulfill this goal by serving as the hub for computational and in silico analyses that translate findings from the three Projects for association and relevance to cardiometabolic traits in humans, thereby enhancing the overall research results of the SCORE. The Core will be led by Dr. Hooman Allayee, whose own research program has developed numerous bioinformatics and analytical pipelines to efficiently and systematically evaluate the role of candidate gene/molecular/biomarker targets in cardiometabolic diseases using large human datasets. Furthermore, the computational infrastructure is in place and access to a wide array of 'omics data is already available from numerous multi-ancestry cohorts. Taken together, these tools have been successfully applied in prior studies and will serve as the paradigm for how the Core will support the three SCORE Projects. Specific Aim 1 will use these resources to investigate sexually dimorphic candidate gene/pathway, transcriptomic, proteomic, and metabolomic biomarkers identified by the three Projects for relevance to human populations. These analyses will leverage primary-level data (i.e. genotypes, clinical outcomes, and biomarker levels) from several population- and hospital-based cohorts with a combined sample size of >500K subjects. As such, epidemiological, genetic, and causal association analyses can be carried out with increased granularity. Specific Aim 2 will complement this approach by leveraging publicly available summary-level data for various `omics data from large-scale genome-wide association studies (GWAS). This strategy will provide even greater power and expand the bioinformatics analyses to additional clinical, proteomic, and metabolomic traits due to the large number of subjects and diverse outcomes in the studies from which the data are drawn. Finally, Specific Aim 3 will create a web-based resource that houses the internally driven and publicly available data from the first two Aims, which will be made available to the three SCORE Projects and, in a controlled manner, to the broader research community. The resource will be developed with PheWeb, an easily implementable and widely used open-source tool for visualizing, navigating, and sharing GWAS and phenome-wide association study (PheWAS) results. The goal will be to provide an intuitive and searchable interface for exploring genetic associations of candidate genes/variants with `omics traits in humans. The culmination of Core activities may, in turn, reveal additional biological information and lead to new hypotheses that can then be investigated mechanistically by the same basic science approaches being used by the three Projects. The collective experience of its leadership/personnel in carrying out the proposed bioinformatics analyses ensures an important contribution from the Core to the overall success of this SCORE.

项目概要 该 SCORE 的总体目标是阐明心脏代谢疾病及其风险的性别差异 使用转基因小鼠模型、近交系小鼠品系以及体外或基于细胞的因素和治疗 实验。然而，SCORE 的另一个重要组成部分是转化模型系统的发现 对人类。人类翻译生物信息学核心将通过充当中心来实现这一目标 计算和计算机分析，将三个项目的研究结果转化为关联性和相关性 人类心脏代谢特征，从而提高 SCORE 的整体研究成果。核心 将由 Hooman Allayee 博士领导，他自己的研究项目开发了许多生物信息学和 高效、系统地评估候选基因/分子/生物标志物作用的分析流程 使用大型人类数据集确定心脏代谢疾病的目标。此外，计算基础设施 已经到位，并且已经可以从众多多血统队列中获取广泛的组学数据。 总而言之，这些工具已成功应用于先前的研究中，并将作为 核心将如何支持三个 SCORE 项目。具体目标 1 将使用这些资源进行调查 鉴定出性二态性候选基因/通路、转录组、蛋白质组和代谢组生物标志物 三个项目与人口的相关性。这些分析将利用初级数据（即 基因型、临床结果和生物标志物水平）来自几个基于人群和医院的队列， 合并样本量超过 50 万受试者。因此，流行病学、遗传和因果关联分析 可以以增加的粒度进行。具体目标 2 将通过利用 来自大规模全基因组关联研究的各种“组学”数据的公开汇总数据 （GWAS）。该策略将提供更强大的力量并将生物信息学分析扩展到更多领域 由于受试者数量众多且结果多样，临床、蛋白质组学和代谢组学特征 从中提取数据的研究。最后，Specific Aim 3 将创建一个基于网络的资源，其中包含 来自前两个目标的内部驱动和公开数据，这些数据将提供给三个目标 SCORE 项目，并以受控的方式面向更广泛的研究界。该资源将是 使用 PheWeb 开发，PheWeb 是一种易于实现且广泛使用的开源工具，用于可视化、导航、 并分享 GWAS 和全表型关联研究 (PheWAS) 结果。目标是提供一个 直观且可搜索的界面，用于探索候选基因/变异与“组学特征”的遗传关联 在人类中。核心活动的高潮反过来可能会揭示额外的生物信息并导致 新的假设可以通过相同的基础科学方法进行机械地研究 被三个项目使用。其领导层/人员在执行拟议的建议方面的集体经验 生物信息学分析确保了核心对该 SCORE 总体成功的重要贡献。