Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and...

Area of Science:

• Genetics
• Neurodevelopmental Disorders
• Computational Biology

Background:

• Autism spectrum disorder (ASD) and intellectual disability (ID) are heritable neurodevelopmental disorders with significant prevalence.
• Numerous genes associated with ASD and ID have been identified, but many remain undiscovered.
• Whole exome sequencing (WES) and whole genome sequencing (WGS) generate vast amounts of genetic data, necessitating prioritization methods.

Purpose of the Study:

• To develop and apply computational methods for prioritizing candidate genes for ASD and ID.
• To identify novel genes associated with these neurodevelopmental disorders.
• To explore shared genetic pathways between ASD and ID.

Main Methods:

• Utilized supervised learning approaches, including network-based and attribute-based classifiers.
• Prioritized candidate genes using curated lists of known ASD and ID genes.
• Incorporated functional information such as protein-protein interactions and pathway associations.

Main Results:

• Successfully ranked and classified known ASD and ID genes, and predicted novel candidates.
• Identified shared pathways between ASD and ID, affecting an overlapping synaptic regulatory subnetwork.
• Demonstrated that neuronal phenotypes in mouse knockouts aid in classifying neurodevelopmental genes.

Conclusions:

• Computational prioritization methods are effective for identifying ASD and ID genes.
• ASD and ID share common biological pathways impacting synaptic regulation.
• These methods can be broadly applied to prioritize genetic variations for various diseases discovered through WES and WGS.