Center for Undiagnosed Diseases at Stanford Administrative Supplement

摘要 尽管基因面板、外显子组和基因组测序(GS)使许多人的分子诊断成为可能 罕见疾病患者中，有相当大比例的患者仍未确诊。一种组合 需要临床专业知识、先进的基因组和多基因组技术以及高效的计算工具 以获得更高的诊断效率。缺乏保险或保险拒绝考试报销进一步带来负面影响 影响来自贫困和经济薄弱背景的罕见疾病患者，使其成为至关重要的 提高罕见病研究队列的覆盖面和多样性。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性，改进方法，并与常规 临床实践。在这里，我们提出一个研究计划，将(1)促进患者的准确诊断 未诊断的疾病，重点是那些没有或有有限保险或经济或语言的人 障碍；(2)在数据分析和不同OME的集成中使用新的方法，以提高诊断能力 (3)加强我们对多样性对诊断过程的影响的理解。在目标1中，我们 建议对患者进行分类和招募，完成临床评估和相关工作。这将包括预付款 就诊表回顾和遗传咨询，然后是个性化的就诊，在此期间标准化 收集了表型和环境数据。生物样本促进基因组、多基因组和细胞学 对疾病的评估。我们将应用新的计算方法来系统地整合基因组， 转录学、代谢学和表型数据与整个医学文献，以提高诊断产量。 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 专注于增加参与者的多样性、参与度和宣传。这将通过扩展 我们当地的患者倡导伙伴关系，包括当地的UDN同龄人小组。我们的目标是增加申请人数 从代表性不足和保险不足的患者，通过区域推广到安全网医院系统和 供应商。此外，我们简化的评价将增加接触服务不足的参与者的机会。最后，我们会 优先安排多语种员工入职，以增强学员体验。

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.