Leveraging human genetics to overcome complex diagnostic challenges, evaluation of pan-ancestry polygenic scores to reduce misdiagnosis of narcolepsy and circadian rhythm sleep wake disorders.

项目摘要/摘要 罕见疾病的最大挑战之一是患者诊断的准确性和及时性。平均而言 需要6年才能做出准确的诊断，延误治疗，并造成严重的负担 个人、家庭和医疗保健系统，估计在不必要的程序上花费了7500亿美元。 误诊还进一步扩大了不平等，因为误诊在妇女和老年人中更常见。 颜色。因此，迫切需要提高诊断水平。遗传风险预测可以提高诊断水平， 在诊断存在很大障碍的特定环境中具有特殊的临床实用性，例如睡眠稀少 精神错乱。罕见的睡眠障碍发作性睡病和昼夜节律睡眠觉醒障碍面临巨大障碍 在诊断方面，20%的初级保健医生没有意识到睡眠医学作为一种专业存在， 睡眠障碍的常规筛查几乎没有，诊断测试需要过夜到有限的 专门诊所的数量和普通诊断测试的保险覆盖范围都不存在。相比之下， 与目前睡眠障碍的诊断格局相比，遗传风险预测相对便宜，而且 交通便利。为了将遗传风险纳入诊断和治疗流程，我们必须首先 让风险的遗传预测指标适用于多个祖先群体，否则我们就有可能扩大 进一步的医疗保健。为了应对睡眠障碍患者及时诊断的挑战，我们建议 利用大型外显子组序列储存库生成罕见的变异风险分数，扩展当前已知的 常见的多基因对多个遗传祖先的评分，并最终测试稀有和 在大型医院预测罕见睡眠和昼夜节律紊乱风险的常见多基因评分 数据库，目标是将标志整合到患者记录中。

Project Summary/Abstract One of the biggest challenges in rare disease is accuracy and timeliness of patient diagnosis. On average it takes 6 years for an accurate diagnosis, delaying treatment and creating substantial burden at the levels of individual, familial and healthcare systems with an estimated $750 billion spent on unnecessary procedures. Misdiagnosis also widens inequalities further, as misdiagnosis is more common among women and people of color. Therefore, there is an urgent need to improve diagnosis. Genetic risk predictions can improve diagnosis, with particular clinical utility in the specific setting where large barriers to diagnosis exist, such as rare sleep disorders. The rare sleep disorders narcolepsy and circadian rhythm sleep wake disorders face large hurdles to diagnosis, where 20% of primary care physicians are unaware that sleep medicine exists as a specialty, routine screening for sleep disorders is nearly absent, diagnostic tests require overnight visits to a limited number of specialized clinics, and insurance coverage for common diagnostic tests is non-existent. In contrast to the current diagnostic landscape for sleep disorders, genetic risk prediction is relatively inexpensive and easily accessible. In order to integrate genetic risk into the diagnosis and treatment pipeline, we must first have genetic predictors of risk applicable across multiple ancestry groups, or we risk widening inequities in healthcare further. To address the challenge of timely patient diagnosis in sleep disorders, we propose to leverage large exome sequence repositories to generate rare variant risk scores, expand the current known common polygenic scores to multiple genetic ancestries, and ultimately test the ability of both the rare and common polygenic scores to predict risk of rare sleep and circadian disorders in a large-scale hospital database with the goal to integrate flags in patient records.