A Systems Biology Approach for Prioritizing ASD Genes in Large or Noisy Datasets

Autism spectrum disorder (ASD) is a complex multifactorial neurodevelopmental disorder. Despite extensive research involving genome-wide association studies, copy number variant (CNV) testing, and genome sequencing, the comprehensive genetic landscape remains incomplete. In this context, we developed a systems biology approach to prioritize genes associated with ASD and uncover potential new candidates. A Protein-Protein Interaction (PPI) network was generated from genes associated to ASD in a public database. Leveraging gene topological properties, particularly betweenness centrality, we prioritized genes and unveiled potential novel candidates (e.g., CDC5L, RYBP, and MEOX2). To test this approach, a list of genes within CNVs of unknown significance, identified through array comparative genomic hybridization analysis in 135 ASD patients, was mapped onto the PPI network. A prioritized gene list was obtained through ranking by betweenness centrality score. Intriguingly, by over-representation analysis, significant enrichments emerged in pathways not strictly linked to ASD, including ubiquitin-mediated proteolysis and cannabinoid receptor signaling, suggesting their potential perturbation in ASD. Our systems biology approach provides a promising strategy for identifying ASD risk genes, especially in large and noisy datasets, and contributes to a deeper understanding of the disorder's complex genetic basis.