The relationships between specific genetic aetiology and phenotype in neurodevelopmental disorders are complex and hotly contested. Genes associated with intellectual disability (ID) can be grouped into networks according to gene function. This study explored whether individuals with ID show differences in autism spectrum characteristics (ASC), depending on the functional network membership of their rare, pathogenic de novo genetic variants. Children and young people with ID of known genetic origin were allocated to two broad functional network groups: synaptic physiology (n = 29) or chromatin regulation (n = 23). We applied principle components analysis to the Social Responsiveness Scale to map the structure of ASC in this population and identified three components—Inflexibility, Social Understanding and Social Motivation. We then used Akaike information criterion to test the best fitting models for predicting ASC components, including demographic factors (age, gender), non-ASC behavioural factors (global adaptive function, anxiety, hyperactivity, inattention), and gene functional networks. We found that, when other factors are accounted for, the chromatin regulation group showed higher levels of Inflexibility. We also observed contrasting predictors of ASC within each network group. Within the chromatin regulation group, Social Understanding was associated with inattention, and Social Motivation was predicted by hyperactivity. Within the synaptic group, Social Understanding was associated with hyperactivity, and Social Motivation was linked to anxiety. Functional network definitions were manually curated based on multiple sources of evidence, but a data-driven approach to classification may be more robust. Sample sizes for rare genetic diagnoses remain small, mitigated by our network-based approach to group comparisons. This is a cross-sectional study across a wide age range, and longitudinal data within focused age groups will be informative of developmental trajectories across network groups. We report that gene functional networks can predict Inflexibility, but not other ASC dimensions. Contrasting behavioural associations within each group suggest network-specific developmental pathways from genomic variation to autism. Simple classification of neurodevelopmental disorder genes as high risk or low risk for autism is unlikely to be valid or useful.

中文翻译：

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智障青年的基因功能网络和自闭症谱系特征：维度表型研究

神经发育障碍中特定遗传病因和表型之间的关系是复杂和激烈的。与智力障碍 (ID) 相关的基因可以根据基因功能分组到网络中。这项研究探讨了 ID 个体是否表现出自闭症谱系特征 (ASC) 的差异，这取决于其罕见的致病性新发遗传变异的功能网络成员。具有已知遗传起源的 ID 的儿童和年轻人被分配到两个广泛的功能网络组：突触生理学 (n = 29) 或染色质调节 (n = 23)。我们将主成分分析应用于社会反应量表，以绘制该人群中 ASC 的结构，并确定了三个成分——不灵活、社会理解和社会动机。然后我们使用 Akaike 信息标准来测试预测 ASC 成分的最佳拟合模型，包括人口统计因素（年龄、性别）、非 ASC 行为因素（全局适应功能、焦虑、多动、注意力不集中）和基因功能网络。我们发现，当考虑到其他因素时，染色质调节组表现出更高水平的不灵活性。我们还观察到每个网络组内 ASC 的对比预测因子。在染色质调节组中，社会理解与注意力不集中有关，社会动机由多动预测。在突触组中，社会理解与多动有关，而社会动机与焦虑有关。功能网络定义是根据多种证据来源手动策划的，但数据驱动的分类方法可能更强大。罕见基因诊断的样本量仍然很小，我们基于网络的组比较方法减轻了这一点。这是一项跨广泛年龄范围的横断面研究，重点年龄组内的纵向数据将为跨网络组的发展轨迹提供信息。我们报告说基因功能网络可以预测不灵活，但不能预测其他 ASC 维度。每个组内的对比行为关联表明从基因组变异到自闭症的特定网络发育途径。将神经发育障碍基因简单分类为自闭症的高风险或低风险不太可能有效或有用。通过我们基于网络的组比较方法来缓解。这是一项跨广泛年龄范围的横断面研究，重点年龄组内的纵向数据将为跨网络组的发展轨迹提供信息。我们报告说基因功能网络可以预测不灵活，但不能预测其他 ASC 维度。每个组内的对比行为关联表明从基因组变异到自闭症的特定网络发育途径。将神经发育障碍基因简单分类为自闭症的高风险或低风险不太可能有效或有用。通过我们基于网络的组比较方法来缓解。这是一项跨广泛年龄范围的横断面研究，重点年龄组内的纵向数据将为跨网络组的发展轨迹提供信息。我们报告说基因功能网络可以预测不灵活，但不能预测其他 ASC 维度。每个组内的对比行为关联表明从基因组变异到自闭症的特定网络发育途径。将神经发育障碍基因简单分类为自闭症的高风险或低风险不太可能有效或有用。我们报告说基因功能网络可以预测不灵活，但不能预测其他 ASC 维度。每个组内的对比行为关联表明从基因组变异到自闭症的特定网络发育途径。将神经发育障碍基因简单分类为自闭症的高风险或低风险不太可能有效或有用。我们报告说基因功能网络可以预测不灵活，但不能预测其他 ASC 维度。每个组内的对比行为关联表明从基因组变异到自闭症的特定网络发育途径。将神经发育障碍基因简单分类为自闭症的高风险或低风险不太可能有效或有用。