Human Translational Bioinformatics Core

项目总结 这项评分的总体目标是阐明心脏代谢性疾病的性别差异及其风险。 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，分数的另一个重要组成部分是翻译模型系统的研究结果 对人类来说。人类翻译生物信息学核心将实现这一目标，成为 计算和计算机分析，将三个项目的结果转化为关联性和相关性 对人体心脏代谢特性的研究，从而提高整体研究成果的得分。《核心》 将由Hooman Allayee博士领导，他自己的研究计划已经开发了许多生物信息学和 有效和系统地评估候选基因/分子/生物标记物作用的分析管道 利用大型人体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血统的队列中获得广泛的组学数据。 总而言之，这些工具已经在以前的研究中成功应用，并将作为 核心将如何支持三个SCORE项目。特定目标1将使用这些资源来调查 确定的性二态候选基因/途径、转录组、蛋白质组和代谢组生物标记物 与人类人口相关的三个项目。这些分析将利用初级数据(即 基因类型、临床结果和生物标记物水平)。 &gt；50万名受试者的综合样本量。因此，流行病学、遗传学和因果关系分析 可以以更高的粒度执行。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可用摘要级数据 (GWAS)。这一战略将提供更大的能力，并将生物信息学分析扩展到其他 临床、蛋白质组和代谢组学特征由于受试者的数量和结果的多样性 这些数据是从研究中得出的。最后，《特定目标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可用的数据，这些数据将提供给三个国家 对项目进行评分，并以受控的方式向更广泛的研究社区提供帮助。该资源将是 使用PheWeb开发，PheWeb是一个易于实现且广泛使用的开源工具，用于可视化、导航 以及共享GWAS和全组关联研究(PheWAS)的结果。目标将是提供一个 用于探索具有组学特征的候选基因/变异的遗传关联的直观和可搜索的界面 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 然后可以通过相同的基础科学方法机械地研究的新假说是 由这三个项目使用。其领导层/人员执行建议的集体经验 生物信息学分析确保了核心对这一得分的总体成功做出了重要贡献。

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.