Liu Yichuan, Qu Hui-Qi, Chang Xiao, Mentch Frank D, Qiu Haijun, Torkamandi Shahram, Nguyen Kenny, Ostberg Kayleigh, Wang Tiancheng, Glessner Joseph, Hakonarson Hakon
Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Mol Psychiatry. 2025 May 24. doi: 10.1038/s41380-025-03065-2.
Despite a significant burden of neurobehavioral and psychiatric comorbidities in children with Down syndrome (DS), and the general increased risk for anxiety in individuals with intellectual disabilities (ID), children with DS have significantly lower odds of anxiety. Understanding the specific mechanisms of anxiety in DS could inform the development of new treatments. This study performed a comprehensive investigation of genomic variants that contribute to anxiety disorders in DS, as well as variants shared in other mental disorders. We employed deep learning algorithms using neural network models in combination with one of the largest whole-genome sequencing (WGS) cohorts of 1479 DS individuals and family members, including 255 DS probands diagnosed with at least one type of mental disorder, of whom 74 had confirmed anxiety disorders. We found that only a fraction (19%) of anxiety-specific corresponding gene variants previously reported overlap with those shared in anxiety in DS patients, suggesting distinct molecular mechanisms for anxiety in DS individuals. Functional overrepresentation analysis suggested that anxiety results from a complex interplay of genetic and environmental factors. Additionally, non-coding variants, particularly those proximal to splicing sites, play significant roles. Moreover, the variants associated with anxiety and other mental disorders are not uniquely distributed genome wide. Several loci, including 17q25, 16q23, 21q22, and 22q13, show greater weight in DS patients. Furthermore, 29 biomarkers containing recurrent anxiety-specific variants were identified to assist in the diagnosis of anxiety in the DS population. This pioneering study represents the first comprehensive exploration of anxiety disorders in DS utilizing WGS cohorts and advanced deep-learning AI models. The results indicate that anxiety disorder in DS patients has distinct molecular patterns from other mental disorders. The insights gained from our research offer valuable understanding of underlying mechanisms and hold promise for enhancing clinical diagnosis and potentially guiding more effective intervention strategies in this vulnerable population.
尽管唐氏综合征（DS）患儿存在显著的神经行为和精神共病负担，且智力障碍（ID）个体普遍存在焦虑风险增加的情况，但DS患儿患焦虑症的几率显著更低。了解DS中焦虑症的具体机制可为新治疗方法的开发提供依据。本研究对导致DS中焦虑症的基因组变异以及其他精神障碍中共享的变异进行了全面调查。我们使用神经网络模型的深度学习算法，结合1479名DS个体和家庭成员的最大全基因组测序（WGS）队列之一，其中包括255名被诊断患有至少一种精神障碍的DS先证者，其中74人确诊患有焦虑症。我们发现，先前报道的仅一小部分（19%）焦虑特异性相应基因变异与DS患者焦虑症中共享的变异重叠，这表明DS个体焦虑症存在独特的分子机制。功能过度表达分析表明，焦虑症是遗传和环境因素复杂相互作用的结果。此外，非编码变异，尤其是那些靠近剪接位点的变异，发挥着重要作用。此外，与焦虑症和其他精神障碍相关的变异并非在全基因组中独特分布。包括17q25、16q23、21q22和22q13在内的几个基因座在DS患者中显示出更大的权重。此外，还鉴定出29种含有复发性焦虑特异性变异的生物标志物，以协助诊断DS人群中的焦虑症。这项开创性研究代表了首次利用WGS队列和先进的深度学习人工智能模型对DS中的焦虑症进行全面探索。结果表明，DS患者的焦虑症具有与其他精神障碍不同的分子模式。我们研究中获得的见解为潜在机制提供了有价值的理解，并有望加强临床诊断，并可能为这一弱势群体指导更有效的干预策略。
