A computational phenotyping approach to characterize neurogenetic disorders

项目总结 在过去的十年里，神经发育的遗传因素呈指数增长。 精神障碍和癫痫。识别了100多个基因，了解表型与特定基因的关系 考虑到发育性大脑疾病的临床复杂性，基因变异是至关重要的。鉴于这种待遇 而预后取决于对基因-表型相关性的理解，迫切需要更好地 评估遗传性癫痫的临床特征。然而，表型分析是一项耗时的手动任务 吞吐量有限。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)，我们之前已经将其应用于SCN2A相关疾病和STXBP1- 相关的障碍，导致已经在临床上应用的知识。我们的长期目标是破译 遗传性癫痫的表型图谱改善临床护理。因此，我们的目标是确定 大样本人群中癫痫相关临床特征与基因组变异的关系 确定20种最常见的遗传性癫痫中的亚组，这些亚组可能提供对结果的洞察和 治疗反应。我们计划通过两个目标来实现这些目标。首先，我们的目标是确定 通过基于HPO的方法(目标1)，对9,000人癫痫表型的基因组特征进行研究。 我们将使用三个外显子组数据和&gT；600K HPO项分析&gt；13K个个体的外显子组数据，以评估 不同的单基因病因和罕见变异与临床癫痫特征的关系 我们团队开发的计算表型工具。这将使我们能够深入了解 遗传病因和颗粒尺度的表型特征。其次，我们定义了相关的子群 通过表型协调治疗遗传性癫痫(目标2)。我们将为20名患者翻译临床特征 最常见的遗传性癫痫的HPO术语并执行语义相似性分析以确定 特定的变种具有显著相似的临床特征，随后进行深入的图表审查。这一知识将会 告知功能研究和临床护理变异的优先顺序。总而言之，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-表型相关性的认识 添加了无与伦比的细节和力量。我们的团队此前曾在 癫痫和神经发育障碍，为我们解决这些问题提供了独特的定位。此外 通过优先处理干细胞或小鼠模型的变种来促进疾病机制的研究， 例如，我们的发现也将通过提供前所未有的精确度来应用于临床护理 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.