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

联合分析基因组和电子病历数据，以评估小儿癫痫的结果和药物反应

• 批准号: 10115148
• 负责人: Ingo Helbig
• 金额: $ 19.19万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10115148
• 关键词: Address; Age; Algorithms; Antiepileptic Agents; Award; Biological; Birth; Child; Clinical; Clinical Course of Disease; Clinical Management; Cohort Studies; Complex; Computerized Medical Record; Computing Methodologies; Data; Data Analyses; Data Set; Data Sources; Development; Developmental Disabilities; Diagnostic; Diagnostics Research; Disease; Early identification; Epilepsy; Funding; Future; Gastaut syndrome; Genes; Genetic; Genetic Predisposition to Disease; Genetic study; Genomics; Genotype; Goals; Hospitals; Human; Independent Scientist Award; Individual; Infantile spasms; Institution; Intractable Epilepsy; Joints; Knowledge; Link; Manuals; Maps; Methods; Mission; Multicenter Studies; Mutation; Myoclonic Astatic Epilepsies; National Institute of Neurological Disorders and Stroke; Neonatal; Neurology; Ontology; Outcome; Outcome Study; Pathway interactions; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Probability; Public Health; Recording of previous events; Research; Research Design; Risk; Seizures; Syndrome; Techniques; Testing; Therapeutic; Therapeutic Intervention; Training; Translating; United States National Institutes of Health; Work; age related; base; biobank; childhood epilepsy; clinical practice; drug development; epileptic encephalopathies; exome; experience; genetic risk factor; genetic variant; innovation; insight; nervous system disorder; novel; phenotypic data; phenotyping algorithm; prevent; programs; rare variant; response; skills; therapy development

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.

项目摘要 多达40％的癫痫儿童对可用的抗癫痫药（AED）没有反应，并识别基因 为了结果和AED响应，将为基础途径提供关键的见解。基因分型很容易成为 对成千上万的患者进行，但表型仍然是一项手动任务。电子医疗 在过去的二十年中，记录（EMR）已实施。此易于使用的数据源已启用 将EMR和生物症剂联系起来以鉴定新型疾病基因的大型研究。但是，EMR数据尚未 到目前为止已用于癫痫遗传研究。长期目标是更好地了解遗传变化 儿童癫痫病预测特定的表型，药物反应和结果。总体目标 这项研究是通过利用EMR数据来识别新的生物学机制来检测遗传危险因素。中央 假设是，虽然儿童癫痫的年龄相关临床模式的复杂性却创造了 产生普遍适用的表型算法的主要障碍，利用替代方法 临床疾病病程和药物轨迹的相似性可用于识别病因 与结果和AED响应相关的变体。这项研究的理由是了解遗传 结果和AED反应的贡献将转化为个性化的药物选择，早期 鉴定有更严重结果的风险的患者，并阐明了新的生物学途径 治疗开发。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，这个 研究将确定与类似的纵向疾病病程相关的遗传因素。初步数据 证明应用计算方法来确定表型的相似性使得 新型遗传病因的鉴定。这项研究将分析2,500的EMR衍生的纵向表型 具有可用遗传数据的个体并确定具有相关疾病轨迹的遗传病因。作为 第二个目的，该研究旨在鉴定影响AED轨迹的遗传因素。 AED响应不容易 从EMR数据集提取。但是，可以比较患者之间的纵向AED历史，这 可能表明生物学确定的共享响应模式。这项研究将测试患者是否罕见 共享基因中的变体具有纵向AED轨迹，这些轨迹比偶然的预期更相似 强调可能表明基因特异性AED反应的经验治疗模式。这种方法是 创新的，因为它利用EMR作为无处不在的，易于访问但以前未经证实的数据源 为了识别儿童癫痫中新型遗传危险因素。拟议的研究很重要，因为 期望扩大对结果和AED反应的遗传危险因素的了解，这是以前 由于表型数据有限，因此无法实现。该建议将申请人的先前经验扩展到大型， 综合队列研究，并为计划的R01申请提供了宝贵的培训。