The SNP rs3747333 and rs3747334 in Neuroligin 4X (NLGN4X) gene have been demonstrated to be associated with the susceptibility to Autism spectrum disorder (ASDs; MIM 209850), but the results are inconsistent. Therefore, a meta-analysis of eligible studies reporting the association between rs3747333 and rs3747334 and ASD was carried out to enhance the reliability of published results. A systematic literature search was performed using PubMed, Web of Science, Cochrane Library to search English articles concerning the relation between rs3747333, rs3747334 and ASD up to Sep. 21th, 2017. Summary odds ratios (OR) and 95% confidence interval (CI) were used to evaluate the risk of rs3747333, rs3747334 in the ASD. The heterogeneity and publication bias of the eligible studies were also evaluated. Six eligible studies involving 1284 subjects (735 patients and 549 healthy controls) were included in this meta-analysis. Overall, the results indicated that there was no significant risk elevation between rs3747333, rs3747334 variants and ASD (OR = 0.39, 95% CI 0.10–1.60). Furthermore, sensitivity analysis and publication bias analysis confirmed this result. In conclusion, our meta-analysis suggests that the rs3747333, rs3747334 in NLGN4X gene are not frequent causes of ASD.

Background: Facial dysmorphism is a critical phenotypic manifestation of many rare genetic disorders, yet systematic and interpretable computational approaches for identifying abnormal facial appearances in pediatric populations remain limited. Methods: We developed a deep learning-based framework for automated screening of pediatric facial anomalies using 434 frontal facial images (235 from genetically confirmed patients, 199 healthy controls). Facial regions were detected using YOLOv8 and processed through standardization and augmentation. Transfer learning with pretrained ResNet18, ResNet34, and ResNet50 architectures was applied for binary classification. Model interpretability was assessed using Gradient-weighted Class Activation Mapping (Grad-CAM). Syndrome-specific models were constructed for Williams–Beuren syndrome (WBS) and Achondroplasia (Ach). Results: On the validation set (n = 132), ResNet18 and ResNet50 achieved perfect classification performance (accuracy, F1 score, AUC = 1.000), while ResNet34 achieved AUC = 0.999. Grad-CAM analysis revealed that ResNet18 consistently focused on clinically relevant craniofacial regions in abnormal cases, with minimal diffuse attention in normal samples. WBS and Ach-specific models achieved AUC = 0.98 with distinct syndrome-specific attention patterns. Conclusion: This framework enables accurate and interpretable identification of pediatric facial anomalies. The syndrome-specific attention patterns demonstrate that the model captures clinically meaningful facial representations, supporting its potential as a computational tool for first-tier screening in rare genetic disorders.