Del Rincón Julia, Gil-Salvador Marta, Lucia-Campos Cristina, Acero Laura, Trujillano Laura, Arnedo María, Pamplona Pilar, Ayerza-Casas Ariadna, Puisac Beatriz, Ramos Feliciano J, Pié Juan, Latorre-Pellicer Ana
Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology and Physiology, School of Medicine, University of Zaragoza, CIBERER-GCV2 and IIS-Aragon-GIIS062, 50009 Zaragoza, Spain.
Clinical and Molecular Genetics Area, Vall d'Hebron Hospital, Medicine Genetics Group, Vall d'Hebron Research Institute (VHIR), 08035 Barcelona, Spain.
Int J Mol Sci. 2025 Aug 18;26(16):7964. doi: 10.3390/ijms26167964.
Despite significant advances in gene discovery, the molecular basis of many rare genetic disorders remains poorly understood. The concept of disease modules, clusters of functionally related genes whose disruption leads to overlapping phenotypes, offers a valuable framework for interpreting these conditions. However, identifying such relationships remains particularly challenging in ultra-rare syndromes due to the limited number of documented cases. We hypothesized that AI-based facial phenotyping could aid in identifying shared molecular mechanisms by detecting phenotypic convergence among clinically related syndromes. To test this, we used Schuurs-Hoeijmakers syndrome (SHMS; OMIM #615009), caused by a recurrent de novo variant in , as a model to explore potential phenotypic and functional associations with -related disorder (DEE66; OMIM #618067) and -related disorder (NOCGUS; OMIM #618652). Facial photographs of individuals with SHMS were analyzed using the DeepGestalt and GestaltMatcher algorithms. In addition to consistently recognizing SHMS as a distinct clinical entity, the algorithms frequently matched DEE66 and NOCGUS, suggesting a shared facial gestalt. Binary comparisons further confirmed overlapping craniofacial features among the three disorders. These findings were supported by literature review, indicating clinical overlapping and potential functional associations. Overall, our results confirm the presence of consistent facial similarities among -, -, and -related syndromes and highlight the utility of AI-driven facial phenotyping as a complementary tool for uncovering clinically relevant relationships in ultra-rare genetic disorders.
尽管在基因发现方面取得了重大进展，但许多罕见遗传疾病的分子基础仍知之甚少。疾病模块的概念，即功能相关基因的簇，其破坏会导致重叠的表型，为解释这些疾病提供了一个有价值的框架。然而，由于记录在案的病例数量有限，在超罕见综合征中识别这种关系仍然特别具有挑战性。我们假设基于人工智能的面部表型分析可以通过检测临床相关综合征之间的表型趋同来帮助识别共享的分子机制。为了验证这一点，我们使用由 中的复发性新生变异引起的舒尔斯 - 霍伊马克斯综合征（SHMS；OMIM #615009）作为模型，来探索与 - 相关疾病（DEE66；OMIM #618067）和 - 相关疾病（NOCGUS；OMIM #618652）的潜在表型和功能关联。使用DeepGestalt和GestaltMatcher算法分析了患有SHMS的个体的面部照片。除了始终将SHMS识别为一种独特的临床实体外，这些算法还经常将DEE66和NOCGUS匹配起来，表明存在共享的面部形态。二元比较进一步证实了这三种疾病之间存在重叠的颅面特征。文献综述支持了这些发现，表明存在临床重叠和潜在的功能关联。总体而言，我们的结果证实了在 -、 - 和 - 相关综合征之间存在一致的面部相似性，并强调了人工智能驱动的面部表型分析作为一种补充工具在揭示超罕见遗传疾病中临床相关关系方面的实用性。 （注：原文中部分基因相关内容缺失具体基因名称，用“ - ”表示）
