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

项目摘要 高达40%的癫痫儿童对现有的抗癫痫药物（AED）没有反应， 结果和AED反应将提供关键的洞察潜在的途径。基因分型很容易 在成千上万的患者身上进行，但表型分析仍然是一项主要的手工工作。电子医疗 电子病历（EMR）在过去的二十年里一直在实施。这一现成的数据源使 将EMR和生物储存库联系起来的大型研究，以确定新的疾病基因。然而，EMR数据并没有 迄今为止，癫痫病的遗传学研究。长期目标是更好地了解基因的变化， 儿童癫痫可预测特定的表型、药物反应和结果。的总体目标 本研究旨在利用电子病历资料，探讨遗传风险因子，以找出新的生物学机制。中央 一种假设是，虽然儿童癫痫的年龄相关临床模式的复杂性产生了一种 产生普遍适用的表型分型算法的主要障碍，利用 临床病程和用药轨迹的相似性可用于确定致病遗传学 与结果和AED反应相关的变异。这项研究的基本原理是， 结果和AED反应的贡献将转化为个性化的药物选择， 确定有更严重结局风险的患者，并阐明 治疗发展。中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 研究将确定与类似的纵向病程相关的遗传因素。初步数据 证明了应用计算方法来确定表型的相似性， 鉴定新的遗传病因。这项研究将分析2500例EMR衍生的纵向表型， 个体可用的遗传数据，并确定遗传病因与相关的疾病轨迹。作为 第二个目的，本研究旨在确定影响AED轨迹的遗传因素。AED响应不容易 从EMR数据集中提取。然而，可以比较患者之间的纵向AED病史， 可以指示生物学上确定的共享响应模式。这项研究将测试是否有罕见的患者 共享基因中的变体具有比偶然预期的更相似的纵向AED轨迹， 突出经验性治疗模式，可能表明基因特异性AED反应。这种方法 创新，因为它利用电子病历作为一个无处不在，易于访问，但以前未经检查的数据源， 以确定儿童癫痫的新遗传危险因素。所提出的研究是有意义的，因为它是 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，不可能。该建议将申请人先前的经验扩展到大型， 综合队列研究，并提供了宝贵的培训，计划在R01应用在今年3该奖项。

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.