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.

项目摘要 该评分的总体目标是阐明心脏代谢疾病及其风险的性别差异 使用遗传修饰的小鼠模型、近交系小鼠品系和体外或基于细胞的 实验然而，SCORE的另一个重要组成部分是从模型系统中转换结果 对人类人类翻译生物信息学核心将通过充当以下中心来实现这一目标： 计算和计算机模拟分析，将三个项目的发现转化为关联和相关性 人类心脏代谢特征，从而提高了SCORE的整体研究结果。核心 将由Hooman Allayee博士领导，他自己的研究计划开发了许多生物信息学， 分析管道，以有效和系统地评估候选基因/分子/生物标志物的作用 使用大型人类数据集研究心脏代谢疾病的靶点。此外，计算基础设施 已经到位，并且已经可以从许多多祖先群体获得广泛的“组学数据"。 总之，这些工具已成功地应用于先前的研究，并将作为范例， 核心将如何支持三个SCORE项目。具体目标1将利用这些资源进行调查 鉴定的性二态候选基因/途径、转录组学、蛋白质组学和代谢组学生物标志物 这三个项目与人类的相关性。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）， 合并样本量> 500 K受试者。因此，流行病学、遗传学和因果关系分析 可以以增加的粒度来执行。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”数据的公开摘要级数据 （GWAS）。这一战略将提供更大的力量，并将生物信息学分析扩展到更多的领域。 临床、蛋白质组学和代谢组学特征，因为受试者数量众多， 这些数据是从哪些研究中得出的。最后，具体目标3将创建一个基于网络的资源， 前两个目标的内部驱动和公开数据，将提供给三个 SCORE项目，并以受控的方式，向更广泛的研究社区。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 用于探索候选基因/变体与“组学特征”的遗传关联的直观且可搜索的界面 在人类身上。核心活动的高潮可能反过来揭示更多的生物信息，并导致 新的假设，然后可以调查机械相同的基础科学方法， 三个项目使用。其领导层/工作人员在执行拟议的 生物信息学分析确保了核心对SCORE的整体成功做出重要贡献。