While genetic studies of epilepsies can be performed in thousands of individuals, phenotyping remains a manual, non-scalable task. A particular challenge is capturing the evolution of complex phenotypes with age. Here, we present a novel approach, applying phenotypic similarity analysis to a total of 3251 patient-years of longitudinal electronic medical record data from a previously reported cohort of 658 individuals with genetic epilepsies. After mapping clinical data to the Human Phenotype Ontology, we determined the phenotypic similarity of individuals sharing each genetic etiology within each 3-month age interval from birth up to a maximum age of 25 years. 140 of 600 (23%) of all 27 genes and 3-month age intervals with sufficient data for calculation of phenotypic similarity were significantly higher than expect by chance. 11 of 27 genetic etiologies had significant overall phenotypic similarity trajectories. These do not simply reflect strong statistical associations with single phenotypic features but appear to emerge from complex clinical constellations of features that may not be strongly associated individually. As an attempt to reconstruct the cognitive framework of syndrome recognition in clinical practice, longitudinal phenotypic similarity analysis extends the traditional phenotyping approach by utilizing data from electronic medical records at a scale that is far beyond the capabilities of manual phenotyping. Delineation of how the phenotypic homogeneity of genetic epilepsies varies with age could improve the phenotypic classification of these disorders, the accuracy of prognostic counseling, and by providing historical control data, the design and interpretation of precision clinical trials in rare diseases.

虽然可以对数千人进行癫痫遗传研究，但表型分析仍然是一项手动的、不可扩展的任务。一个特殊的挑战是捕捉复杂表型随年龄的演变。在这里，我们提出了一种新方法，将表型相似性分析应用于来自先前报道的 658 名遗传性癫痫患者队列的总共 3251 患者年的纵向电子病历数据。在将临床数据映射到人类表型本体之后，我们确定了从出生到最大 25 岁的每个 3 个月年龄间隔内共享每种遗传病因的个体的表型相似性。所有 27 个基因中的 140 个（23%）和 3 个月的年龄间隔具有足够的数据来计算表型相似性，显着高于偶然的预期。27 种遗传病因中有 11 种具有显着的整体表型相似性轨迹。这些不仅反映了与单一表型特征的强统计关联，而且似乎来自复杂的临床特征群，这些特征群可能不会单独强烈关联。作为在临床实践中重构证候识别认知框架的尝试，纵向表型相似性分析通过利用电子病历中的数据扩展了传统的表型分析方法，其规模远远超出了手动表型分析的能力。描述遗传性癫痫的表型同质性如何随年龄变化可以改善这些疾病的表型分类、预后咨询的准确性，并通过提供历史对照数据，