A computational phenotyping approach to characterize neurogenetic disorders

项目摘要 在过去的十年里，神经发育障碍的遗传原因呈指数级增长。 疾病和癫痫。随着100多个基因的鉴定，了解表型如何与特定的 考虑到发育性脑疾病的临床复杂性，遗传变异至关重要。鉴于这种治疗 预后取决于对基因型-表型相关性的理解，因此迫切需要更好地 评估遗传性癫痫的临床特征。然而，表型分型是一项耗时的手动任务， 有限的吞吐量。为了克服这一瓶颈，我们开发了一种新的方法，基于人类 表型本体论（HPO），我们以前应用于SCN 2A相关疾病和STXBP 1- 相关的疾病，导致知识已经应用于临床。我们的长期目标是破译 遗传性癫痫的表型景观，以改善临床护理。因此，我们的目标是确定 在一个大的患者群体中，基因组变异和癫痫相关临床特征之间的关系， 确定20种最常见的遗传性癫痫中的亚组，这些亚组可以提供对结果的深入了解， 治疗反应。我们计划通过两个目标来实现这些目标。首先，我们要确定 通过基于HPO的方法，在> 9，000名个体中对癫痫表型的基因组特征进行分析（目标#1）。 我们将分析具有三个外显子组数据和> 600 K HPO术语的> 13 K个体中的外显子组数据，以评估 不同单基因病因和罕见变异与临床癫痫特征之间的关系，使用 我们团队开发的计算表型分析工具。这将有助于深入了解 遗传病因学和表型特征在颗粒尺度上。其次，我们的目标是定义相关的子群 遗传性癫痫通过表型协调（目标#2）。我们将翻译20个临床特征 最常见的遗传性癫痫HPO术语，并执行语义相似性分析，以确定是否 特定的变体具有显著相似的临床特征，然后进行深入的图表审查。这些知识将 为功能研究和临床护理提供变体优先级信息。总之，基于HPO的划分 遗传性癫痫的研究有望通过以下方式显著提高对基因型-表型相关性的认识： 添加无与伦比的细节和力量。我们的团队以前曾在计算表型分析方面处于领先地位， 癫痫和神经发育障碍，使我们能够解决这些问题。此外 为了通过优先考虑用于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，提供前所未有的精确度， 预后和治疗信息。

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.