Center for Undiagnosed Diseases at Stanford Administrative Supplement

摘要 尽管基因组、外显子组和基因组测序（GS）已经使许多人的分子诊断成为可能， 罕见病患者中，有相当大比例的患者仍未得到诊断。的组合 需要临床专业知识、先进的基因组学和多组学技术以及高效的计算工具 以获得更高的诊断率。缺乏保险或保险拒绝报销测试费用 影响来自贫困和经济薄弱背景的罕见疾病患者， 改善罕见疾病研究队列的覆盖范围和多样性。未诊断疾病中心（CUD） 在斯坦福大学，我们将继续努力实现可持续发展，改进方法，并与常规的整合。 临床实践在这里，我们提出了一个研究计划，将（1）促进患者的准确诊断 未确诊的疾病，重点是那些没有或有限的保险或经济或语言 障碍;（2）在数据分析和不同'omes的整合中使用新方法，以改善诊断 率;（3）提高我们对多样性对诊断过程的影响的理解。目标1： 建议分流和招募患者，完成临床评估和相关工作。这将包括预- 访问图表审查和遗传咨询，然后进行个性化访问，在此期间， 收集表型和环境数据。生物样品有助于基因组、多组和细胞 疾病的评估。我们将应用新的计算方法进行基因组的系统整合， 转录组学、代谢组学和表型数据与整个医学文献相结合，以提高诊断率。 我们将继续努力利用现有资源为所有参与者寻求诊断。在目标2中，我们将 注重增加参与者的多样性、参与度和宣传。这将通过扩大 我们当地的患者倡导伙伴关系，包括当地的UDN PEER小组。我们的目标是增加应用程序 通过区域外展服务，将代表性不足和保险不足的患者转移到安全网医院系统， 提供商此外，我们简化的评价将增加获得服务不足的参与者的机会。最后我们将 优先安排多语种工作人员入职，以增强参与者的体验。

ABSTRACT Although gene panels and exome and genome sequencing (GS) have enabled molecular diagnosis for many rare disease patients, a significantly large proportion of these patients remain undiagnosed. A combination of clinical expertise, advanced genomic and multi-omic technologies and efficient computational tools is needed for higher diagnostic yield. Lack of insurance or denial of test reimbursement by insurance further negatively impacts rare disease patients from underprivileged and economically weak backgrounds, making it critical to improve the outreach and diversity in rare disease study cohorts. The Center for Undiagnosed Diseases (CUD) at Stanford will continue our efforts toward sustainability, refinement of methods, and integration with regular clinical practice. Here, we propose a program of study that will (1) facilitate accurate diagnosis of patients with undiagnosed diseases, with emphasis on those without or with limited insurance or economical or language barriers; (2) use novel approaches in data analysis and integration of different ‘omes to improve diagnostic rates; and (3) enhance our understanding of the impact of diversity on the diagnostic process. In Aim 1, we propose to triage and enroll patients, complete clinical evaluations and associated work. This will include pre- visit chart review and genetic counseling followed by an individualized visit during which standardized phenotypic and environmental data are collected. Biosamples facilitate genomic, multi-omic, and cellular evaluation of disease. We will apply novel computational approaches for systematic integration of genomic, transcriptomic, metabolomic, and phenotypic data with the entire medical literature to improve diagnostic yield. We will continue our efforts to seek diagnosis using available resources for all participants. In Aim 2, we will focus on increasing participant diversity, engagement, and advocacy. This will be accomplished by expanding our local patient advocacy partnerships, including a local UDN PEER group. We aim to increase applications from underrepresented and underinsured patients through regional outreach to safety net hospital systems and providers. Further, our streamlined evaluations will increase access to underserved participants. Finally, we will prioritize onboarding multilingual staff to enhance participant experience.