Autism Spectrum Disorder (ASD) and Intellectual Disability (ID) are comorbid neurodevelopmental disorders with complex genetic architectures. Despite large-scale sequencing studies only a fraction of the risk genes were identified for both.
Researchers presented 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. Their model leverages information from gene co-expression networks that model human brain development using graph convolutional neural networks and learns which spatio-temporal neurovelopmental windows are important for disorder etiologies. Their approach approach substantially improves the state-of-the-art prediction power in both single-disorder and cross-disorder settings. DeepND identifies mediodorsal thalamus and cerebral cortex brain region and infancy to childhood period as the highest neurodevelopmental risk window for both ASD and ID.
They observed 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, they investigated 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 and is released at http://github.com/ciceklab/deepnd.
bioRxiv Subject Collection: Neuroscience