Joint analysis of genomic and electronic medical record data to assess outcomes and drug response in pediatric epilepsies

项目总结 高达40%的癫痫儿童对现有的抗癫痫药物(AEDs)没有反应，并识别基因 对于结果和AED反应将提供对潜在途径的关键洞察。基因分型可以很容易地 对数万名患者进行了基因分型，但表型鉴定在很大程度上仍然是一项人工任务。电子医疗 记录(EMR)已在过去20年中实施。这个现成的数据源已启用 将EMR和生物信息库联系起来以识别新的疾病基因的大型研究。然而，电子病历数据并没有 到目前为止已被用于癫痫遗传学研究。长期目标是更好地了解基因如何在 儿童期癫痫可以预测特定的表型、药物反应和结果。总的目标是 本研究旨在利用EMR数据检测遗传风险因素，以确定新的生物学机制。中环 假设是，虽然儿童癫痫与年龄相关的临床模式的复杂性创造了一个 生成普遍适用的表型算法的主要障碍，替代方法利用 临床病程和用药轨迹的相似性可以用来确定致病基因 与结局和AED反应相关的变量。这项研究的基本原理是，理解基因 早期对结果和AED反应的贡献将转化为个性化的药物选择 确定有可能出现更严重后果的患者，并阐明新的生物学途径 治疗的发展。核心假设将通过追求两个具体目标来检验。作为第一个目标，这 研究将确定与类似纵向病程相关的遗传因素。初步数据 演示了应用计算方法来确定表型的相似性 新的遗传病因的鉴定。这项研究将分析2500名来自EMR的纵向表型 拥有可用的遗传数据的个人，并通过相关的疾病轨迹确定遗传病因。作为一名 第二个目的，本研究旨在确定影响AED轨迹的遗传因素。AED的反应并不容易 从电子病历数据集中提取。然而，患者之间的纵向AED病史可以进行比较， 可能表明生物决定的共享反应模式。这项研究将测试罕见的患者是否 共享基因中的变体具有纵向的AED轨迹，这些轨迹比预期的更相似， 强调可能表明基因特异性AED反应的经验性治疗模式。这种方法是 创新，因为它利用EMR作为无处不在的、易于访问的、但以前未检查过的数据源 目的：确定儿童癫痫新的遗传危险因素。这项拟议的研究具有重要意义。 预计将扩大对结果和AED反应的遗传风险因素的理解，这在以前是 由于表型数据有限，这是不可能的。这项建议将申请人的先前经验扩展到大型， 综合队列研究，并为本奖项第三年计划中的R01申请提供宝贵的培训。

PROJECT SUMMARY Up to 40% of children with epilepsy do not respond to available antiepileptic drugs (AEDs), and identifying genes for outcome and AED response will provide critical insight into underlying pathways. Genotyping can readily be performed on tens of thousands of patients, but phenotyping remains a largely manual task. Electronic medical records (EMR) have been implemented over the last two decades. This readily available data source has enabled large studies linking EMR and biorepositories to identify novel disease genes. However, EMR data have not been used in epilepsy genetic studies so far. The long-term goal is to better understand how genetic changes in childhood epilepsies predict specific phenotypes, medication responses, and outcomes. The overall objective of this study is to detect genetic risk factors by utilizing EMR data to identify new biological mechanisms. The central hypothesis is that while the complexity of the age-related clinical patterns of the childhood epilepsies creates a major obstacle in generating universally applicable phenotyping algorithms, alternative methods leveraging the similarity of the clinical disease course and medication trajectory can be used to identify causative genetic variants associated with outcome and AED response. The rationale of this study is that understanding the genetic contribution for outcome and AED response will translate into personalized medication choices, early identification of patients at risk for a more severe outcome, and elucidation of novel biological pathways for therapy development. The central hypothesis will be tested by pursuing two specific aims. As a first aim, this study will determine genetic factors associated with a similar longitudinal disease course. Preliminary data demonstrates that applying computational methods to determine the similarity of phenotypes enable the identification of novel genetic etiologies. This study will analyze EMR-derived longitudinal phenotypes in 2,500 individuals with available genetic data and identify genetic etiologies with related disease trajectories. As a second aim, this study aims to identify genetic factors that influence AED trajectories. AED response is not easily extracted from EMR datasets. However, longitudinal AED histories between patients can be compared, which may indicate biologically determined shared response patterns. This study will test whether patients with rare variants in shared genes have longitudinal AED trajectories that are more similar than expected by chance, highlighting empirical treatment patterns that may indicate gene-specific AED responses. This approach is innovative, as it leverages EMR as a ubiquitous, easily accessible, but previously unexamined data source in order to identify novel genetic risk factors in childhood epilepsies. The proposed research is significant as it is expected to expand understanding of genetic risk factors for outcome and AED response, which was previously not possible due to limited phenotypic data. This proposal extends the prior experience of the applicant to large, integrated cohort studies and provides invaluable training for a planned R01 application in in Year 3 of this award.