A computational phenotyping approach to characterize neurogenetic disorders

表征神经遗传疾病的计算表型方法

• 批准号: 10635575
• 负责人: Ingo Helbig
• 金额: $ 75.52万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-15 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10635575
• 关键词: Address; Adolescent; Anticonvulsants; Brain Diseases; Caring; Case Series; Case Study; Child; Chorea; Classification; Clinical; Clinical Data; Complement; Complex; Consumption; Data; Development; Diagnosis; Dictionary; Disease; Epilepsy; Etiology; Future; Genes; Genetic; Genetic Predisposition to Disease; Genomics; Genotype; Goals; Human; Independent Scientist Award; Individual; Infantile spasms; Inherited; Intractable Epilepsy; Knowledge; Life; Link; Manuals; Methods; Natural History; Neurodevelopmental Disorder; Ontology; Other Genetics; Outcome; Parents; Patients; Pattern; Phenotype; Positioning Attribute; Prognosis; Public Health; Recurrence; Research; Risk; Risk Assessment; SCN8A gene; Seizures; Semantics; Sodium Channel Blockers; Subgroup; Time; Translating; United States National Institutes of Health; Variant; Vulnerable Populations; Work; childhood epilepsy; clinical care; cohort; de novo mutation; developmental disease; disability; early onset; exome; gain of function; genetic variant; genomic variation; improved; insight; loss of function; medical vulnerability; mouse model; neurogenetics; novel strategies; precision medicine; prevent; rare variant; response; stem cell model; tool; treatment response; trial design

PROJECT SUMMARY Over the last decade, there has been an exponential increase in identified genetic causes of neurodevelopmental disorders and epilepsy. With more than 100 genes identified, understanding how phenotypes relate to specific genetic variants is critical, given the clinical complexity of developmental brain disorders. Given that treatment and prognosis is dependent on understanding genotype-phenotype correlations, there is a critical need to better assess clinical features in genetic epilepsies. However, phenotyping is a time-consuming, manual task with limited throughput. To overcome this bottleneck, we have developed a novel approach, based on the Human Phenotype Ontology (HPO), which we have previously applied to SCN2A-related disorders and to STXBP1- related disorders, resulting in knowledge that is already applied clinically. Our long-term goal is to decipher the phenotypic landscape of genetic epilepsies to improve clinical care.Therefore, our objectives are to determine the relationship between genomic variation and epilepsy-related clinical features in a large patient cohort and to identify subgroups within the 20 most common genetic epilepsies that may provide insight into outcomes and treatment responses. We plan to pursue these objectives through two aims. First, we aim to determine the impact of genomic features on epilepsy phenotypes in >9,000 individuals through an HPO-based approach (Aim #1). We will analyze exome data in >13K individuals with trio exome data and >600K HPO terms to assess the relationship between distinct monogenic etiologies and rare variants with clinical epilepsy features, using computational phenotyping tools developed by our team. This will allow for insight into the relationship between genetic etiologies and phenotypic features at a granular scale. Secondly, we aim to define relevant subgroups in genetic epilepsies through phenotype harmonization (Aim #2). We will translate clinical features for the 20 most common genetic epilepsies to HPO terms and perform a semantic similarity analysis to determine whether specific variants have significantly similar clinical features, followed by in-depth chart review. This knowledge will inform the prioritization of variants for functional studies and clinical care. In summary, HPO-based delineation of genetic epilepsies is expected to significantly improve knowledge of genotype-phenotype correlations by adding unmatched detail and power. Our team has previously pioneered computational phenotype analysis in the epilepsies and neurodevelopmental disorders, positioning us uniquely to address these questions. In addition to facilitating research of disease mechanisms by prioritizing variants for work with stem cells or mouse models, for example, our findings will also apply to clinical care by providing an unprecedented level of precision in prognosis and treatment information.

项目摘要 在过去的十年中，鉴定出神经发育的遗传原因呈指数增长 疾病和癫痫。鉴定出100多个基因，了解表型与特定的关系如何 鉴于发育性脑部疾病的临床复杂性，遗传变异至关重要。鉴于这种治疗 预后取决于理解基因型 - 表型相关性，迫切需要更好 评估遗传性癫痫中的临床特征。但是，表型是一项耗时，手动的任务 有限的吞吐量。为了克服这种瓶颈，我们基于人类开发了一种新颖的方法 表型本体论（HPO），我们以前已应用于SCN2A相关疾病，并将其应用于STXBP1- 相关疾病，导致已经在临床上应用的知识。我们的长期目标是破译 遗传癫痫的表型景观以改善临床护理。因此，我们的目标是确定 大型患者队列中的基因组变异与癫痫相关的临床特征之间的关系 在20个最常见的遗传性癫痫中识别亚组，这些遗传癫痫可能可以洞悉结果和 治疗反应。我们计划通过两个目标来追求这些目标。首先，我们旨在确定影响 通过基于HPO的方法（AIM＃1），在> 9,000个个体中癫痫表型的基因组特征。 我们将分析具有三重奏外显节数据和> 600k HPO术语的> 13k个个体中的外显子组数据，以评估 使用临床癫痫特征的不同单基因病因与稀有变体之间的关系，使用 我们团队开发的计算表型工具。这将使您深入了解 遗传病因和表型特征以颗粒状尺度。其次，我们旨在定义相关子组 在遗传性癫痫中通过表型统一（目标＃2）。我们将翻译20个临床特征 最常见的遗传性癫痫对HPO术语并执行语义相似性分析，以确定是否是否 特定的变体具有明显相似的临床特征，然后进行了深入的图表审查。这些知识会 告知用于功能研究和临床护理的变体的优先级。总之，基于HPO的描述 预计遗传性癫痫将显着提高基因型 - 表型相关性的知识 添加无与伦比的细节和力量。我们的团队以前已经开创了计算表型分析 癫痫病和神经发育障碍，将我们定位为独特的解决这些问题。此外 通过优先考虑使用干细胞或小鼠模型的变体来促进疾病机制的研究， 例如，我们的发现也将适用于临床护理，通过提供前所未有的精度 预后和治疗信息。