Translating genomic profiling to bedside precision medicine

抽象的。该计划将在莫尔豪斯学院建立一个新的基因组学卓越中心 医学，使HBCU能够建立一个全面的基因组学倡议，专注于提高 代表不足的少数族裔学生(URM)学生在基因组研究培训中的代表性， 加强对教师的再培训，其重点是减少少数群体的健康差距。 我们与我们在埃默里大学、佐治亚州理工学院的合作伙伴一起提出了这个CEGS项目 技术、亚特兰大儿童保健和橡树岭国家实验室。我们把重点放在 研究基因组技术在护理点效用方面的应用，并特别关注其影响 种族、性别和年龄对测试准确性的影响。该计划将刺激教育和培训的发展 URM学生在亚特兰大大学中心的机会(亚特兰大有4个HBCU)，因此 加强对来自URM的基因组研究人员的培训和再培训。 该中心研究部分的中心前提是血液转录组分析可以 提供一种创新的方法来诊断多种临床情况下的疾病或损伤。这项研究的核心是 通过了解该技术方法的关键要素并将其推向临床实践 学习可概括的实施规则。为此，我们使用了三项相互关联的研究，分别是关于健康、急性 脑损伤和长期心血管疾病，以测试重复性和预测准确性。我们利用 建立了研究计划，以实现对这些方法的快速测试，以及 橡树岭国家实验室利用人工智能数学建模来提高预测精度。在… 在研究计划结束时，我们将建立一系列传播和 未来该方法的使用情况。具体研究目标为1)。基因表达的差异是什么？ 血？2)。确定血液RNA表达谱预测急性胰腺炎临床诊断的准确性 医疗事件？3)。血液RNA表达特征能识别患者的结局或疾病轨迹吗 (预后)？RNA表达对患者预后或疾病轨迹(预后)的影响是否与， 独立于健康的社会决定因素(SDH)，或受其影响。 为了有效地组织项目的执行，我们建立了一个研究管理结构来 支持对关键的通过/不通过决策进行持续评估。将这项研究转化为增强版 关于教育机会和再培训，我们建议建立一个教育核心，重点是适应 设立临床研究硕士课程，为学生和工作人员开发新的培训模式 计算技能和生物信息学，这是一个促进调查员发展的小额赠款计划。我们的决赛 目标是开始从本科院校开发一条管道，以加强生物信息学和基因组学 培训和研究机会，以帮助使目前的基因组学工作场所多样化。

ABSTRACT. This program will establish a new Center for Excellence in Genomics at Morehouse School of Medicine, enabling a comprehensive genomics initiative to be established at a HBCU focused on increasing the representation of under-represented minority students (URM) students in genomics research training, enhancing retraining of faculty and whose focus is the reduction of health disparities in minority populations. We propose this CEGS program in collaboration with our Partners at Emory University, Georgia Institute of Technology, Children’s Health Care of Atlanta, and the Oak Ridge National Laboratories. We focus the research on the application of genomic technologies to point of care utility, with a specific focus on the impact of race, sex, and age on testing accuracy. The program will spur the development of education and training opportunities for URM students withing the Atlanta University Center, (4 HBCUS in Atlanta) and therefore enhance the training and retraining of genomic researchers from URMs. The central premise of the research component of this center is that blood transcriptome analysis may offer an innovative approach to diagnose disease or injury for many clinical conditions. The gal of this study is to push this technology approach closer to clinical practice by understanding key elements of the approach and to learn generalizable rules for implementation. To do this we use three interlinked studies on healthy, acute brain injury and long-term cardiovascular disease, to test reproducibility, and prediction accuracy. We utilize established research programs to enable a rapid testing of the approaches, and the computational expertise of the Oak Ridge National Laboratories to harness AI mathematic modeling to enhance prediction accuracy. At the end of the research program, we will have established a series of best practices for dissemination and future use of the approach. The specific research aims are 1). What is the variance of gene expression in blood? 2). Determine the accuracy of blood RNA expression profile to predict the clinical diagnosis of an acute medical event? 3). Can a blood RNA expression signature identify a patient’s outcome or disease trajectory (prognosis)? Is RNA expression’s effect on patient’s outcome or disease trajectory (prognosis) associated with, independent of, or modified by social determinants of health (SDH). To organize the efficient performance of the program, we establish a research management structure to enable continuous assessment with key go/no go decisions. To translate this research into enhanced education opportunities and retraining, we propose an education core that will focus on adapting an established Masters of Clinical Research course, developing new training modules for students and staff in computational skills and bioinformatics, a small grant program to enhance investigator development. Our final goal is to start to develop a pipeline from undergraduate colleges to enhance bioinformatics and genomics training and research opportunities to help diversify the current genomics workplace.