Establishment and validation of a phenotype-driven predictive model for the diagnostic efficacy of trio-based whole exome sequencing (trio-WES) in children with genetic neurodevelopmental disorders (g-NDDs) - Wu, Ruohao; Luo, Xiangyang; Meng, Zhe; Tang, Wenting; Liang, Liyang (Frontiers in Neurology, 2025)
BackgroundGenetic neurodevelopmental disorders (g-NDDs), a complex group of idiopathic syndromes with a heterogeneous genetic etiology, are defined as global developmental delay/intellectual disability (GDD/ID) with other common neurodevelopmental comorbidity (NDC), such as autism spectrum disorder (ASD). Although significant progress has been made in trio-based whole-exome sequencing (trio-WES) that enable the detection of exon-level variants, the diagnostic efficacy of using trio-WES for g-NDDs is still not satisfactory. Therefore, exploring key phenotypic variables for forecasting the diagnostic probability of trio-WES is extremely necessary to implement personalized diagnosis for children with g-NDDs.MethodsA total of 265 g-NDDs children who received trio-WES at Sun Yat-sen Memorial Hospital between Sep 2016 and Dec 2022 were enrolled and clustered temporally into training and internal validation sets [163 cases (Oct 2016 ~ Dec 2022) and 102 cases (Sep 2016 ~ Sep 2018), respectively]. A total of 97 g-NDDs children who underwent trio-WES at Weierkang Children’s Rehabilitation Center between Jan 2023 and Dec 2023 were enrolled and served as an independent external validation set. Univariate and multivariate logistic regression were conducted in the training set to screen out independent diagnosis-related phenotypic signifiers and establish an alignment diagram model. The model was further validated in internal and external validation sets.ResultsThrough univariate and multivariate analyses, independent diagnosis-related predictive signifiers, including GDD/ID severity, NDC complexity, ASD, and head circumference abnormality, were identified in the training cohort and used to construct a model. The model showed good discrimination power with an area under the ROC curves (AUC) in the training set of 0.821 (95% CI: 0.756–0.886), yielding a F1 score of 0.76. The model also showed powerful prediction in both the internal (AUC: 0.905 with 95% CI: 0.842–0.968 and F1 score: 0.77) and external validation sets (AUC: 0.919 with 95% CI: 0.858–0.979 and F1 score: 0.79).ConclusionWe found the potential linear relationship between trio-WES-diagnostic rates and the phenotypic enrichments in g-NDDs patients for the first time, indicating the possibility of applying a logistic regression model based on phenotypic features to predict the personalized diagnostic rates of using trio-WES in children with g-NDDs.