Multitask learning of gene risk for autism spectrum disorder and intellectual disability
Çiçek, Abdullah Ercüment
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Autism Spectrum Disorder (ASD) and Intellectual Disability (ID) are comorbid neurodevelopmental disorders with complex genetic architectures. Despite largescale sequencing studies only a fraction of the risk genes were identified for both. Here, we present a novel network-based gene risk prioritization algorithm named DeepND that performs cross-disorder analysis to improve prediction power by exploiting the comorbidity of ASD and ID via multitask learning. Our model leverages information from gene co-expression networks that model human brain development using graph convolutional neural networks and learns which spatiotemporal neurodevelopmental windows are important for disorder etiologies. We show that our approach substantially improves the state-of-the-art prediction power. We observe that both disorders are enriched in transcription regulators. Despite tight regulatory links in between ASD risk genes, such is lacking across ASD and ID risk genes or within ID risk genes. Finally, we investigate frequent ASD and ID associated copy number variation regions and confident false findings to suggest several novel susceptibility gene candidates. DeepND can be generalized to analyze any combinations of comorbid disorders.
KeywordsAutism spectrum disorder
Graph convolutional networks