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<p>Differentiating autism spectrum disorder (ASD) from developmental delay (DD) is critical for guiding early intervention, but overlapping features and shared biological mechanisms pose challenges. This study investigates whether copy number variations (CNVs) affecting serotonergic genes carry sufficient information to distinguish between these neurodevelopmental disorders (NDDs). Using network mapping and machine learning, we applied gene ontology (GO) terms related to serotonergic systems to filter CNVs and construct networks modeling genetic and biological variation in ASD and DD. We identified hub nodes and subnetworks reflecting distinct patterns in gene and GO term interactions. ASD networks analysis yielded six genetic clusters, five of which remarkably contained genes specifically linked to serotonergic receptor mechanisms. In contrast, DD networks exhibited greater genetic homogeneity, with just two clusters sharing serotonergic mechanisms. Random Forest classifiers using serotonergic gene features achieved an average prediction accuracy of 85.6%, increasing to 88.6% when combined with dopaminergic dosage features, consistent with the two systems capturing partially non-overlapping biological signal. GO-based features yielded comparable accuracy with fewer inputs, emphasizing their efficiency. These findings demonstrate that different genetic alterations may be associated with disruption of shared biological pathways, each leaving a distinct signature tied to clinical diagnoses. Importantly, although genetic heterogeneity is observed in ASD, we found a homogeneity of serotonergic biological terms, suggesting convergence of mechanisms, which was distinct for each condition. Together, these results suggest that serotonergic CNVs carry discriminatory information for ASD vs. DD classification, and that combining serotonergic and dopaminergic features captures partially non-overlapping biological signal.</p>

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Network analysis of serotonin CNVs shows biological convergence from genetic heterogeneity and discriminates between autism and developmental delay

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • André Santos,
  • Francisco Caramelo,
  • Joana Barbosa Melo,
  • Miguel Castelo-Branco

摘要

Differentiating autism spectrum disorder (ASD) from developmental delay (DD) is critical for guiding early intervention, but overlapping features and shared biological mechanisms pose challenges. This study investigates whether copy number variations (CNVs) affecting serotonergic genes carry sufficient information to distinguish between these neurodevelopmental disorders (NDDs). Using network mapping and machine learning, we applied gene ontology (GO) terms related to serotonergic systems to filter CNVs and construct networks modeling genetic and biological variation in ASD and DD. We identified hub nodes and subnetworks reflecting distinct patterns in gene and GO term interactions. ASD networks analysis yielded six genetic clusters, five of which remarkably contained genes specifically linked to serotonergic receptor mechanisms. In contrast, DD networks exhibited greater genetic homogeneity, with just two clusters sharing serotonergic mechanisms. Random Forest classifiers using serotonergic gene features achieved an average prediction accuracy of 85.6%, increasing to 88.6% when combined with dopaminergic dosage features, consistent with the two systems capturing partially non-overlapping biological signal. GO-based features yielded comparable accuracy with fewer inputs, emphasizing their efficiency. These findings demonstrate that different genetic alterations may be associated with disruption of shared biological pathways, each leaving a distinct signature tied to clinical diagnoses. Importantly, although genetic heterogeneity is observed in ASD, we found a homogeneity of serotonergic biological terms, suggesting convergence of mechanisms, which was distinct for each condition. Together, these results suggest that serotonergic CNVs carry discriminatory information for ASD vs. DD classification, and that combining serotonergic and dopaminergic features captures partially non-overlapping biological signal.