Ganesan Shiva, Ruggiero Sarah M, Parthasarathy Shridhar, Galer Peter D, Lewis-Smith David, McSalley Ian, Cohen Stacey R, Lusk Laina, Prentice Anna J, McKee Jillian L, Pendziwiat Manuela, Smith Lacey, Weber Yvonne, Mefford Heather C, Poduri Annapurna, Helbig Ingo
Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, United States.
Deptartment of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, United States.
bioRxiv. 2025 Mar 12:2025.03.11.642649. doi: 10.1101/2025.03.11.642649.
Genomic sequencing is widely used to identify causative genetic changes in neurodevelopmental disorders, such as autism, intellectual disability, and epilepsy. Most neurodevelopmental disorders also present with diverse clinical features, and delineating the interaction between causative genetic changes and phenotypic features is a key prerequisite for developing personalized therapies. However, assessing clinical features at a scale that parallels genomic sequencing remains challenging. Here, we standardize phenotypic information across 11,125 patient-parent trios with exome sequencing data using biomedical ontologies, analyzing 674,767 phenotypic terms. We find that individuals with variants in 69 out of 261 neurodevelopmental genes exhibit statistically significant clinical similarities with distinct phenotypic fingerprints. We also observe that phenotypic relatedness follows a gradient, spanning from highly similar to dissimilar phenotypes, with intra-gene similarities suggesting clinically distinct subgroups for seven neurodevelopmental genes. For most genetic etiologies, only a small subset of highly phenotypically similar individuals carried variants in the same gene, highlighting the heterogeneous and complex clinical landscape of neurodevelopmental disorders. Our study provides a large-scale overview of the dynamic relationship between genotypes and phenotypes in neurodevelopmental disorders, underscoring how the inherent complexity of these conditions can be deciphered through approaches that integrate genomic and phenotypic data.
基因组测序被广泛用于识别神经发育障碍（如自闭症、智力障碍和癫痫）中的致病基因变化。大多数神经发育障碍还表现出多样的临床特征，而阐明致病基因变化与表型特征之间的相互作用是开发个性化治疗方法的关键前提。然而，以与基因组测序相匹配的规模评估临床特征仍然具有挑战性。在这里，我们使用生物医学本体对11125个有外显子测序数据的患者-父母三联体的表型信息进行标准化，分析了674767个表型术语。我们发现，261个神经发育基因中有69个基因存在变异的个体表现出具有统计学意义的临床相似性，且具有独特的表型指纹。我们还观察到，表型相关性呈梯度变化，从高度相似到不同表型，基因内相似性表明七个神经发育基因存在临床上不同的亚组。对于大多数遗传病因，只有一小部分表型高度相似的个体在同一基因中携带变异，这突出了神经发育障碍的异质性和复杂的临床情况。我们的研究提供了神经发育障碍中基因型与表型之间动态关系的大规模概述，强调了如何通过整合基因组和表型数据的方法来解读这些疾病的内在复杂性。
