PT - JOURNAL ARTICLE AU - Ilayda Beyreli AU - Oguzhan Karakahya AU - A. Ercument Cicek TI - Multitask learning on comorbid disorders improves gene risk prediction for Autism Spectrum Disorder and Intellectual Disability AID - 10.1101/2020.06.13.150201 DP - 2020 Jan 01 TA - bioRxiv PG - 2020.06.13.150201 4099 - http://biorxiv.org/content/early/2020/06/15/2020.06.13.150201.short 4100 - http://biorxiv.org/content/early/2020/06/15/2020.06.13.150201.full AB - 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. 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 spatio-temporal neurovelopmental windows are important for disorder etiologies. We show that our 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. 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 and is released at http://github.com/ciceklab/deepnd.Competing Interest StatementThe authors have declared no competing interest.