Prioritization of ASD-candidate genes by a systems biology approach
Authors:
Veronica Remori (1,2), Heather Bondi (1), Manuel Airoldi (1), Diana Carli (3,4), Giulia Borini (3), Chiara Davico (5), Lisa Pavinato (3,6,7), Emanuela Nobile (8), Roberta Marinoni (9), Eleonora Di Gregorio (9), Roberto Keller (8), Alessandro Musso (3), Giovanni Battista Ferrero (10), Alfredo Brusco (3,9), Mauro Fasano (1,11)
Organisations:
1: Department of Science and High Technology (DiSAT), University of Insubria, Como
2: Department of Information Engineering and Mathematics, University of Siena, Siena
3: Department of Medical Sciences, University of Torino, Torino
4: Immunogenetics and Transplant Biology, AOU Città della Salute e della Scienza, Torino
5: Department of Public health and Pediatrics, University of Torino, Torino
6: Institute of Oncology Research (IOR), BIOS+, Bellinzona, Switzerland
7: Università della Svizzera Italiana, Lugano, Switzerland
8: Adult Autism Center, DSM Local Health Unit, Torino
9: Medical Genetics Unit, AOU Città della Salute e della Scienza, Torino
10: Department of Clinical and Biological Sciences, University of Torino, Torino
11: Center of Research in Neuroscience, University of Insubria, Busto Arsizio
Presenting author:
Veronica Remori
Exposition date:
Friday, September 15, 2023
Exposition position:
010/1
Autism spectrum disorders (ASD) are complex neurodevelopmental disorders with a strong genetic component. To unravel the genetic complexity of ASD, several large-scale genome-wide association studies, copy number variants (CNVs) testing, and genome sequencing have been performed, resulting in the identification of many candidate variants.
In this context, computational methods that prioritize candidate genes based on additional functional information can be valuable. We hypothesized that possible candidate autism risk genes can be identified through their connections with known autism risk genes. Here, we propose a systems biology approach to prioritize candidate ASD-associated genes. From the Simons Foundation Autism Research Initiative (SFARI) Gene database, an input list of 750 non-syndromic genes associated with ASD with high confidence or strong candidate gene ratings (with SFARI score 1 or 2, respectively) was collected. We generated a protein-protein interaction (PPI) network that included first interactors of the SFARI genes, using the IMEx database (nodes = 12896, edges = 305609), and performed topological analysis.
As a proof of concept, we mapped a list of genes with CNVs of unknown significance identified through array-Comparative Genomic Hybridization (CGH) analysis in 128 ASD patients on PPI-network. A prioritized list of genes was obtained by ranking the gene list by betweenness centrality score. We are currently performing over-representation analysis (ORA) using WebGestalt to assess whether genes in the prioritized list were significantly associated with metabolic or disease pathways. Furthermore, we are implementing our approach with an unsupervised classification to strengthen the identification of novel candidate genes.
Although the relevance of these genes needs to be further validated in functional studies, our approach offers a promising strategy for identifying potential ASD risk genes and may contribute to our understanding of the genetic basis of ASD.
